Category Archives: Career Paths and Job Reports

How To Be A Data Scientist: The Comprehensive Guide

BY Roger Huang

What is Data Science?

Data scientist roles are often one of the most highly-paid and highly-rewarding jobs out there. Glassdoor has cited data scientist at the #1 position for most-satisfying job in the United States. With the explosive growth of unstructured data, there has never been a greater need for data scientists.

This has prompted a wave of questioning about how to be a data scientist, with upwards of 600 people a month searching for that on Google.

IBM predicts demand for data scientists will increase 28% by 2020. Data science roles are among LinkedIn’s fastest and most growing emerging fields with about 650% growth since 2012.

Data science combines statistical knowledge, programming chops and domain expertise/communication skills. You’ll work on dealing with large amounts of data and get as much insight at scale as you can.

Job Prerequisites

To become a data scientist, you have to have a solid understanding of statistics, mathematics and the theory behind different algorithms.

You also have to have enough programming chops, usually in a language such as Python or R to iterate with data science models.

You also have to be able to communicate your findings to top executives. You need to have enough domain expertise to understand your data and the implications of it.

Typically, most roles will need advanced degrees and programming experience. STEM degrees are preferred. However, some companies will hire undergraduates straight from school — and advanced degrees, while preferred, are not a hard prerequisite. You can do data science without a PhD or even a Master’s degree.

Data Science Salary

Based on a Kaggle survey, data scientists and the adjacent field of machine learning engineers earn the highest median salary ($120,000 USD) in the United States of America. Australia closely follows at about $110,000 USD. Other countries fall swiftly down the median, with data scientists earning close to $15,000 USD in both Russia and India.

While it’s clear that you can earn a lot being a data scientist, it’s also true that there are nuances.

The division in the United States makes this clear. States like California and New York have the highest volume of data science jobs. California data scientists average about $140,000 USD in yearly salary. Washington and New York State follow up in the $115,000 USD to $120,000 USD range. New Jersey, Maryland, and North Carolina are around there as well.

California is home to Silicon Valley and the growing startups in San Francisco. Washington state headquarters both Microsoft and Amazon. New York state and adjacent states like New Jersey host large vibrant startup ecosystems including Silicon Alley. While all these figures need to be adjusted for cost of living (different states like Kansas come first due to their low cost of living in another analysis), they show a key tenet of raw data science salaries: to earn as much as possible, you’ll have to go to where data is most valuable.

Factors That Increase Your Data Science Salary

I wrote this handy guide from Springboard on factors that increase your data science salary after doing some research. The most important ones are the data science tools you have experience with, the industry you work in, the location you choose to work in (as discussed above), the data science role you choose, your experience and degrees, and the individual negotiation for each salary.

Understanding big data tools like Spark and data visualization tools like D3.js, a powerful and advanced custom library for strong visualization might increase your yearly salary by between $8,000 USD and $15,000 USD.

It’s not just data science in general that drives your salary, it’s also the individual components you’re familiar with. Premiums are paid for data scientists who know how to handle large amounts of data in a distributed fashion, and those who can work with powerful data visualization libraries.

If you have up to 15 data science tools mastered, it can increase your salary about $30,000 USD.

You’ll also want to work in an industry that has access to a lot of valuable data. This tends to be software or social media companies who pay the highest for data scientists (think Facebook or Google).

You’ll want to make sure you’re working as a data scientist or data engineer, not a data analyst. Most intro-level roles in the data space are data analyst roles. It will affect your future salary if you stay in data analyst roles or only apply to them.

As discussed, your location is key as well. If you want the absolutely highest raw salary, you’ll have to move to the United States, and you’re likely going to be working in one of the tech hubs there (either San Francisco/Silicon Valley, New York City, or the DC area). However, you should note that the amount of salary you can gain on location, while high, may not be as high as other factors that don’t need you to move.

Finally, your level of experience can make a dramatic difference. Having ten or more years of experience can add around $30,000 USD to your yearly salary as a data scientist. And while degrees might not be a hard prerequisite, those with advanced degrees do tend to earn more as data scientists.

Data Science Curriculum/Checklist

First, you’ll want to start with enough programming knowledge so you can play around with the different concepts and libraries. In practice, a lot of the statistics and mathematics is abstracted away by different programming libraries. It’s best to learn some of the basics of statistics and programming at the same time. If you had to focus on one area, start with the programming practice.

Most machine learning and data science libraries (including Pandas, Numpy and scikit-learn, the mainstays of data science) are compiled in Python. You’ll want to start there, and work with Anaconda so you can manage different packages and dependencies. Once you’re in, you can find different courses to practice your Python programming, and practice live in the Jupyter Notebook offered, which is an intuitive and easy-to-access editor for code that can be run locally and uploaded or given version control quite easily by hooking it up with Git and a Github account.

Here’s the documentation for how to get started with Anaconda and Jupyter notebook. The following post summarizes different ways of working with Jupyter notebooks and version control. Finally, this post from freeCodeCamp explains Git and the importance of version control.

While you can work on Jupyter Notebook in a local context by yourself and seldom do anything but upload your finished experiments and files to Github (something I’ve often done), building in the habit of working with version control is a great practice.

It’s the default method of collaboration between different programmers, who must ensure that code doesn’t conflict — so if you want to work on a data science team, or any software team for that matter, it’s always good to start with good habits.

You’ll also want to use version control to revert back in case something goes wrong and to maintain a steady thread of progress.

Programming

R vs Python

A large part of the data science ecosystem debate is whether or not to use R or Python as an intro-level programming language to get started. In this article for The Next Web, I wrote that it was ideal to know both. Realistically, if you had to choose, I would go with Python. We’ll start there, but I’ll add some R resources in case.

R

R on Codecademy

Codecademy can help you practice your R skills before you start applying it to data science use cases.

Introduction to R for Data Science

This interactive course is given by Microsoft on the edX platform, and is completely free to access. You will need to pay $99 USD if you want to have a verified certificate on your profile.

Python

49 Essential Resources to Learn Python

I wrote this list of resources to learn Python, going from beginner to advanced. Go through and pick out the resources that are data science and machine learning-specific.

Learning Python: From Zero to Hero

A text-based tutorial that summarizes the basics of Python. It will get you from knowing zero to Python hero.

Python Tutorial: Learn Python For Free | Codecademy

Codecademy was how I learned Python. Working through the interactive course modules will help you move through and learn by doing and practicing.

SQL

21 of the Best Free Resources to Learn SQL

You’ll want to practice your SQL as well if you’re looking to become a data scientist. A large amount of data is still held in structured SQL tables. Practicing with SQL will help you extract that data and work with it.

SQLZoo

SQLZoo works partly as a Wiki, partly as a set of interactive exercises. I use this to sharpen my SQL skills when I need to practice.

Pandas

Pandas dataframes are the default unit of data wrangling in data science work. Pandas allows you to organize your data in a tabular, structural fashion similar to a SQL table or an Excel spreadsheet. It also allows you to use Python to programmatically treat data.

Pandas Cookbook

This handy guide goes over the Pandas library and different things you can do with it from grouping to aggregation functions. It’s a handy interactive guide to Pandas — and it’s how I first started getting familiar with the library and data science in general.

A Comprehensive Guide to Data Wrangling

This guide helps define data wrangling, why it’s important, and introduces a few new functions and situations in Pandas to get you comfortable with it.

Statistics

Once you’re able to source data, you’ll need the statistical ability to be able to draw insight from the data you’ve collected.

As you’re learning the programming you need, you need to be able to understand statistics to manipulate data, understand it, and evaluate different models. This often involves at least a basic understanding of probability, frequentist and Bayesian statistics.

Statistics and Probability: KhanAcademy

This interactive video-filled course will help you catch up on frequentist statistics, confidence intervals, p-values, and more. It’ll serve as a refresher if you’ve encountered these concepts in university, and a learning opportunity if you haven’t.

A Concrete Introduction to Probability by Peter Norvig

This iPython Notebook allows you to directly work with probability concepts in your own version of Jupyter Notebook should you desire. It expresses probability ideas in very readable Python code, helping to combine both your programming practice and statistics knowledge.

Bayes’s Theorem: A Visual Introduction

This post introduces Bayesian theory with a lot of visualizations. It can take the visualizations to really crystalize Bayesian thinking, especially since it involves a lot of segmentation on probability.

Introduction to Bayesian Inference

This tutorial uses a Python library to explain Bayesian reasoning through a model of click-throughs on ads. Use it to understand Bayesian inference in practice.

Mathematics

Once you’re done with the statistics, it can be good to understand some of the mathematics behind data science and machine learning even if most of the detail is not something you’ll confront everyday given how abstracted away most of the math is.

The Mathematics of Machine Learning

The Towards Data Science article sums up the categories of mathematics you need to learn as well as links to different courses.

Mathematics of Machine Learning

This book is offered as a free PDF, covering several sections of machine learning math in detail from analytic geometry to vector calculus.

Machine Learning

Now that you’ve refreshed or embraced statistics and programming concepts, it’s time to take it all together and learn the machine learning algorithms you can use on your data.

A Tour of Machine Learning Algorithms

Starting with foundational concepts in machine learning such as the difference between supervised and unsupervised learning (and semi-supervised) we can then drill down into the different categories of machine learning algorithms and broadly see how the logic works with a set of visualizations.

10 Machine Learning Algorithms You Need To Know

This Towards Data Science Medium post then dives a bit deeper into ten specific machine learning algorithms, giving code implementations of a few so you can see them in practice on data.

Data Modelling/Evaluation

After all the work on different algorithms, it’s time to refresh what makes for a good data model. How do you know if your model is working? This section of resources will help you put that together.

Part-4 Data Science Methodology From Modelling to Evaluation

The article summarizes the data science methodology. In this section, it focuses on how evaluating your model fits with the broader work of machine learning and data science.

Various ways to evaluate a machine learning model’s performance

The following tutorial includes a breakdown of evaluation metrics beyond accuracy such as the confusion matrix and the ROC curve.

Data Visualization

Python Matplotlib Guide

Matplotlib is the default data visualization library embedded in Python, and something designed to be used off-the-bat with Pandas. You can use its visualizations to get a quick sense of the data yourself without needing to export it. This guide goes over the basics of Matplotlib and how it’s constructed.

Visualization With Seaborn

Seaborn is a Python library that provides more compelling data visualizations than the default Matplotlib library. Use this tutorial to get familiar with it.

Intro to D3.js with ten examples

This D3.js introduction helps get you started with the powerful JavaScript library. The related chart collection helps collect tons of examples of different charts you can use to visualize your data in R, Python and more.

Datasets to Practice With

Datasets | Kaggle

Kaggle, the online data science competition platform, offers a variety of datasets you can use to practice your data science skills. The datasets feature ranking and comments so you can follow the most trending datasets. You can study what others have done with them as inspiration for your own projects.

19 Free Public Data Sets for Your Data Science Project

The link above is a list of 19 free, public datasets ranging from United States census data to FBI crime data.

Awesome Public Datasets

A Github repository that hosts a wide variety of open, public databases. They are organized by their domain. This is a great definitive resource for free datasets.

Awesome IPFS Datasets

This website hosts datasets, some of them quite large, hosted on IPFS (the interplanetary file system). This is a distributed, decentralized protocol of storing data that goes beyond HTTP’s standard server-client relationships. In theory, this means that datasets downloaded through IPFS might be faster to get. After all, you’ll be working with a swarm of hosts rather than just a single one.

Registry of Open Data on AWS

Amazon Web Services, which helps host much of the content on the web today, also has this registry that helps people find open data hosted on its cloud services. It includes examples of what people have done with that data.

BigQuery Open Datasets

Google hosts the above datasets on BigQuery, its big data storage solution. They include the complete revision history of Wikipedia up to April 2010, and weather information from the NOAA since late 1929.

Data Science Courses/Bootcamps

The curriculum might be a bit too much to handle as a learner — and that’s perfectly fine. It’s meant as a bare-bones categorization of the material you need to learn to get into data science. However, if you want to refine your learning, there are a few options out there. I’ve linked to a list of bootcamps and courses. Be aware that I worked for Springboard.

Data Science Bootcamps

Data Science Bootcamps, CourseReport

CourseReport has a list of different data science bootcamps, with ratings and real student reviews given for each course.

Springboard

Springboard offers a variety of mentored bootcamps where you’re given personal attention from a data science expert and career coaching. It also comes with a job guarantee. Either get a job or your tuition back once you’re accepted.

Udacity

Udacity offers nanodegrees where you can dive deep into specific data science topics such as self-driving cars.

Data Science Courses

Coursera (Data Science)

Coursera offers a variety of online course options for data science. Use them well to deepen your learning in the field.

Udemy Data Science

Udemy offers a variety of data science courses created by different independent teachers on its platform.

Data Science Interview Questions

The data science interview tends to fall into many steps, with some being technical and some being non-technical. I wrote this guide for Springboard on the data science interview process to fully flesh it out. I’ve added some sample questions you might expect, some with solutions, under each section.

Initial Recruiter Call

Before you’re assessed by a hiring manager, you’ll usually have a call with a recruiter to determine if you’re a fit with the company. They’ll ask general questions about your motivations and career path and see if you’re a fit with what the hiring manager wants.

Sample questions

Why this company?
What interests you about the role?
What are your salary expectations?

Technical Interview

A hiring manager will ask you technical questions related to your knowledge of statistics and programming. Here are about 109 data science questions with solutions. For programming, you can try HackerRank challenges as well to stay sharp before your interview.

Technical Case Study

Part of the interview process will involve either an in-depth review of a project you worked on or a case study where you work with your (hopefully) future team. This will involve detailed questions about work you’ve done or how you’d approach a project. You might have to do a take-home assignment or to work on a problem with the hiring manager.

Behavioral Interview

The behavioral part of the interview will test your management and communication skills as well as fit with the team. It’s usually done by the hiring manager rather than the recruiter.

Job Boards And Resources

There are many data science specific job resources and career sites out there worth following. Here are a few where you can find resources and data science job postings.

KDNuggets

KDNuggets features a job board and many resources for aspiring data scientists and the community at large.

Kaggle

Kaggle features a host of different resources for data scientists, including datasets that are free and public for use, a customized version of a Python kernel that allows for automated version control as well as collaboration with other Kaggle users and a host of competitions that can help you practice and show your data science skills.

Data Elixir

Data Elixir is a newsletter dedicated to data science resources and jobs. Sign up to get a periodical update on the data science ecosystem.

Data Science Weekly

Another great newsletter filled with breaking news and tons of learning opportunities. Data Science Weekly is well worth subscribing to.

Hacker News Jobs

Hacker News Jobs is a great spot to cleanly aggregate machine learning and data science job positions from technologists who post on Hacker News “Who’s Hiring?” threads.

What’s great about these postings is that you’ll often find a lot of context and a direct connection to a hiring manager, who will often leave their email directly on a posting to make themselves available for connection. You can easily search for data science specific postings.

Angellist Jobs

AngelList is the world’s largest repository of startups, many of whom are looking to hire for data science roles. You can filter specifically for data science roles, location and industry.

Do You Need A Degree Or Not?

This is an ongoing discussion. Advanced degrees help increase your data science salary and some hiring managers display a bias towards those degrees. Many hiring positions demand a minimum of a bachelor’s degree.

However, DJ Patil, the fromer Chief Data Scientist of the United States, called on recruiters and companies to judge candidates based on what they did with data, not their education.

While the data science community often draws from the same ethos of do-it-yourself learning-by-doing that typifies the open source community, it can be a more gated process because of the statistics and math knowledge needed, as well as the communication skills data scientists need to develop.

Work experience can fill a lot of gaps here, but to get into the industry, it’s possible you might have to start with a data analyst role then move up in a data scientist role, or settle for a junior data science role or internship if you have no experience and no degree.

Despite the emergence of Masters programs targeted for data science, the truth is that you don’t absolutely need a degree to succeed in data science.

Sample Data Science Job Roles

Data Scientist – Personalization @ Spotify

This role at Spotify involves a lot of teamwork and data exploration. It focuses on data modeling. Data engineers help to bring pipelines of data for you to model properly. This role is more focused on the product analytics team, and as a result, is cross-functional in nature. While there is a demand for degrees, most of the other requirements involve applied experience.

Entry Level Data Scientist @ IBM

This entry level role doesn’t need a degree — rather just the skills that make up the data science curriculum. The focus is on communication, tools, and the different models that make up data science roles.

Candid Data Science Career Advice On How To Be A Data Scientist

Here’s some candid career advice from different data scientists in the field to help you with how to be a data scientist:

Claire Longo (Senior Machine Learning Engineer @ Twilio): Beat imposter syndrome by choosing a focus area to master. Talk about the stuff you don’t know as well as the stuff that you do.

Jess Zhang (Inference Data Scientist @ Airbnb): Throw out the first number and do your research when it comes to negotiations. Look through courses and continually refresh and learn so you have a toolbox you can rely on. Find somebody who believes in you, sometimes through networking at data science meetups.

Anmol Rajpurohit (Senior Software Engineer @ Splunk Enterprise Cloud): Data science isn’t for everybody. Make sure you know what you’re getting into before you start a career in data science.

Checklist On How To Be A Data Scientist

1- Learn basic statistics, including frequentist, Bayesian thinking and probability theory.

2- Learn how to programmatically source and organize data, preferably with Python.

3- Learn more the more advanced statistics and mathematics behind data science at scale, from linear algebra, to model evaluation.

4- Practice your learning and work on projects with production-level datasets. Build a portfolio for hiring managers.

5- Prepare for the data science job interview process.

6- Accept a data science job offer (after many months of effort, most likely).

7- Continually practice!

Blockchain Jobs Report (How to get a blockchain job)

BY Roger Huang

This job report has been sponsored by Blockgeeks – For courses on how to become a blockchain developer please visit Blockgeeks.com

Intro

By now, you’ve probably heard of the growing blockchain and cryptocurrency sector and you’re probably wondering how to get blockchain jobs. It seems to have turned the world upside down. As Bitcoin peaked at a price past $20,000 in late 2017, a mad fever seemed to descend on the whole technology industry.

With both Bitcoin and Ethereum pushing the sector forward with large partnerships, institutional money flowing in, and a wave of technical advancements, the sector is maturing. That brings entrepreneurial talent and demand for new hires.

New projects are now creating their own tokens to be able to raise money and hire teams to execute on their product roadmap. Now is a good time to examine blockchain jobs.

The difference between blockchains and cryptocurrencies

Before we get started, we have to define a few terms.

Blockchains are distributed blocks of data linked together by a specific set of consensus protocols, while cryptocurrencies are the tokens used to access services and applications built on the blockchains in questions. For example, you use the Ether token to access services on the Ethereum blockchain.

You can trade in tokens so they may increase or decrease in price as a function of the overall health of the blockchain they access.

In practice, a cryptocurrency startup will be focused on the value of the token and any underlying application and blockchain they build on top, while a blockchain startup will be focused on building or maintaining blockchains while not necessarily taking advantage of tokens for internal compensation or for fundraising.

This will matter when it comes to compensation, with blockchain startups geared towards conventional salaries and cryptocurrency startups offering slightly higher pay, and token grants to incentivize employees. There is a difference between blockchains and cryptocurrencies after all.

A look at the sector

Are there new blockchain and cryptocurrency jobs? What are the types of companies building products in the space?

Most blockchain and cryptocurrency startups have the same demands for different roles as your standard startup. Web development, product and design needs in blockchain and cryptocurrency are as strong as they are in any regular startup. There are now thousands of open job roles with different blockchain/cryptocurrency startups hosted on popular startups job site AngelList.

Consultancy firms working in the space have flagged a talent gap. Deloitte alone is looking to hire about 25,000 people who are familiar with the blockchain space. Developers can command salaries of over $220,000 USD.

There are currently 401 blockchain and cryptocurrency startups (as of July, 30th, 2018) hiring for full-time roles above $100k USD in compensation on popular startups site AngelList. Using the same search approach, about 800 cryptocurrency and blockchain startups are currently hiring for different roles.

A quick look at CoinMarketCap shows there’s about $200bn in market value in cryptocurrency as of August 2018. There are about 2,000 cryptocurrencies listed, many of them supported by a core company.

About 196 or almost 25% of all cryptocurrency startups on AngelList are hiring for a role that requires JavaScript skills. 55 or about 6.25% are hiring for digital marketing skills.

About half of all blockchain and cryptocurrency startups on AngelList have tagged themselves as being open to remote job roles.

This compares with the 30% average on AngelList of all companies that are open to remote roles.

Cryptocurrency and blockchain startups are on average almost twice as likely as your standard tech startup to accept distributed team members and remote job roles.

There are also some freelance roles in cryptocurrency and blockchains, with many workers in the space listed on platforms like Upwork. There are, as of August 2018, about 1000 freelancers listed with blockchain as a term.

In comparison to more established careers such as web development and data science, there are less absolute jobs. However, the sector and the jobs attached to it are growing rapidly.

Compensation

Blockchain developers in the United States earn about $130,000 USD according to ComputerWorld. This is a bit higher than the average for programmers everywhere at $105,000.

Freelance rates for different cryptocurrency functions can be higher than $150 USD per hour.

Increasing numbers of cryptocurrency and blockchain startups use their own cryptocurrency tokens to compensate employees. Think of these as more liquid versions of startup shares, with more liquid markets where you can sell your tokens at different prices.

Why blockchain/crypto?

You might want to choose the sector and get interested in blockchain jobs because it’s growing and there’s higher compensation. You might also choose it because you want to upend traditional hierarchies.

The number of blockchain jobs and crypto jobs posted on AngelList, a popular startups job resource, nearly quadrupled between 2016 and 2017.

There are three main reasons to join the sector.

You want higher-than-average compensation that is more liquid (albeit with quite some risk).
You want to learn about blockchains and cryptocurrencies
You want a flexible/distributed relationship with your work.

Sample job types

Designer

There is a need for designers throughout a crypto startup. They help with making any project look good with a consistent brand identity. They can ensure a great user experience and help the adoption curve of new crypto products come off flawlessly.

This job description from Crypto Jobs List and Messari help shows what a designer role at a crypto startup looks like.

Developer

There different types of software developers in crypto-land. You have the same web development needs as any tech startup would have. You also have a demand for people who are familiar with how to build blockchains or create smart contracts. These demands are often separated. You have developers focused on on-chain work that is more related to the blockchain and its specifics. You also have off-chain developers focused on standard web development tasks.

The above is from a job posting for a blockchain engineer. The focus is on Solidity development and on-chain work and writing smart contracts. Tools such as Truffle and Web3.js that are specific to the Solidity environment come into play.

Meanwhile, this role above is for more of a standard front-end developer role for a cryptocurrency startup. It’s much more focused on providing interface work for smart contracts rather than dealing with the on-chain contract work itself.

Software engineers will find plenty of blockchain jobs available to them, should they have the right mentality and the thirst to learn more about the sector.

Community Management/Investor Relations

Cryptocurrencies are marketed with the strength of their communities. Think of ICOs as a sort of Kickstarter for companies: the strength of your backing community and the network it forms largely determines how much you raise. Most crypto startups will have robust communities focused on Reddit, Telegram, Discord, Slack and traditional social media.

Community management roles in blockchain/crypto fall closer to a combination of customer support and marketing. Community managers play a critical role in the marketing of a cryptocurrency startup, both in terms of curating real use cases and in driving early investors or supporters to broadcast updates.

A subset of community management involves dealing with high-net-worth individuals who form the bulk of ICO investment, coming closer to investment management.

Marketing/Sales

ICO projects will often look at digital marketers to get growth going, both in terms of the whitepaper and product goals but also to get investor interest from the crowd.

As you can see with the sample job role, a lot of the marketing and growth work for cryptocurrency startups is related to community management.

In general, blockchain and cryptocurrency companies that are looking to get user adoption for their products will need growth marketers and salespeople to help. Blockchain technology is just one part of the fight — user adoption is another.

Compliance

ICOs and crypto startups have to deal with a whole slew of legal issues. One wrong move, and massive fees and legal issues will arise. There is often the need to hire legal help, whether internal or external.

This role above is an example of an in-house counsel role. A lot of focus is placed on legal issues specific to cryptocurrency, especially anti-money laundering regulations.

Sample companies

There are different types of blockchain and cryptocurrency startups you can work for.

There are startups that have created their own cryptocurrency and are building an ecosystem and products related to it. You might think of such startups as Request Network, Quantstamp and more. However, there are many projects outside of just name-brand companies. Every company that is raising money through an ICO is probably hiring.

There are startups that use a layer of cryptocurrency to help them carry out key parts of their business. The web browser Brave is an ads-free browser that uses its own cryptocurrency, Basic Attention Token, in order to compensate content creators and aggregators.

There are startups, companies, and organizations building their own blockchains or using different blockchains. An example of somebody building their own blockchain is the DFINITY project. There are also popular blockchains such as Stellar and Ripple.

There are startups building tools that make it easier for people to exchange, trade and secure cryptocurrencies such as cryptocurrency exchanges like Coinbase, over-the-counter trading tools such as Circle, and cryptocurrency portfolio trackers like Blockfolio. There are also people providing financial services in cryptocurrencies — funds such as Polychain Capital.

There are non-for-profit and open source collaborations such as the Ethereum Foundation and Hyperledger.

Conventional companies like IBM are looking to hire blockchain engineers to help them understand the space.

Finally, there are accelerators or holding companies that co-own multiple cryptocurrency or blockchain ventures and act as a supervising entity. The largest of these is probably Ethereum-based ConsenSys.

Locations

Most cryptocurrency roles are in certain hotspots: Singapore, San Francisco and Zug Valley in Switzerland, for example.

Distributed teams are a force for cryptocurrency startups, with half of all cryptocurrency startups willing to hire for remote roles.

You can work for cryptocurrency startups remotely or on contract without having to move through different borders. This is a fitting extension of a technology that aimed to lower borders for transactions of value.

You are as likely to work in your own home as in an office located in a cryptocurrency hotspot.

Skills/knowledge needed

The standard startup skillset is essential for blockchain jobs. The archetypes of design, development, and business (hipster, hacker, and hustler) shine through.

There is a lot of demand for people who have experience building production-level blockchain projects because it’s a very rare skill set. The ability to work on blockchain projects outside of the Bitcoin Core team has been relatively new. It came with the explosion of Distributed Apps and Ethereum-based applications.

The more experience developers have with different blockchains, the better — as the blockchain space is highly volatile, with different blockchains iterating and developing and some vanishing off the map entirely. It makes it much easier to get blockchain jobs if you have exposure to different blockchains.

You’ll often have a combination of C++/Golang experience for blockchain development, and if you’re working with Ethereum-based DApps (distributed apps), you’ll likely need your standard JavaScript toolkit (the MEAN stack, with Node.js as a back-end) as well as knowledge in Solidity. Other languages are present (for example, the Python-based Serpent for Ethereum development) but C++/Golang and JavaScript/Solidity are the two main skillsets in demand. The standard software development stack often prevails, with blockchains such as Bitcoin residing on Github.

On the business and marketing side, those who have had effective experience running paid ads, and creating organic communities will fit right in. However, cryptocurrency marketing comes with an additional landmine of paying attention to legal and compliance issues. There is a sweet spot for customer service/community management types who can corral the vast organic communities most cryptocurrencies sport on Telegram, Reddit and Discord.

Designers are needed for constructing interfaces and what are called “off-chain” applications. In many ways, most blockchain and cryptocurrency startups have the same need for interface designers as your standard tech startup — so there are plenty of blockchain jobs for designers.

Resources/learning paths for skillsets/knowledge

I’m going to split out here curated learning paths on two dimensions, one focused on the business side of cryptocurrency, the other on the technical part. We’ll have a curated set of learning resources for both.

This will make it easier for you to have specific knowledge and examples of the biggest problems encountered in both blockchain and cryptocurrency and help you get a handle on what blockchain jobs require.

Business/non-technical

Introduction to blockchain and cryptocurrency

Crypto Canon

The Crypto Canon is venture capital firm Andreessen Horowitz (a16z)’s effort to curate some of the best articles on blockchain and cryptocurrency. It goes from building blocks and basics to developer tutorials.

A definitive glossary of blockchain and cryptocurrency terms

This glossary will help you navigate different words and terms in the blockchain space, and will also help you come up with a curated understanding of the non-technical elements of the space.

Governance

An important part of the blockchain discussion revolves around who controls the blockchain in question and the meaning of decentralization. The following resources will help clarify this topic.

The meaning of decentralization

This article by Vitalik Buterin, Ethereum’s creator, helps explore what is really meant by decentralization — and helps tease out the differences between centralization on an architectural or technological basis, and centralization in a political structure.

A deep dive into governance

This slide deck talks about why governance itself is important, before also breaking down what makes blockchain governance so different from traditional models of governance.

Privacy

Blockchains are pseudonymous by default. Wallet balances are public but not who owns the wallet. They are not completely private unless that is deliberately designed. Anybody who can track down the identity behind a Bitcoin wallet can trace their every transaction. This was how the FBI seized Silk Road’s assets.

Privacy on the Blockchain

This article discusses how privacy on the blockchain is an uphill battle and suggests a few key weaknesses in the privacy approach of most bitcoin users. It then suggests a few solutions that might help.

zk-SNARKs

Using alternatives to Bitcoin can address privacy concerns. ZCash, a cryptocurrency focused on using zero-knowledge proofs to guarantee privacy writes about how it can prove something is valid without revealing any information about it: the basis of a truly private blockchain.

Scale

Another major issue is scale: how many transactions per second a blockchain can process. This becomes a major issue as Bitcoin or Ethereum surge in usage but cannot support the number of transactions. Ethereum can process about 13-15 transactions per second, but Visa can process up to 56,000 transactions per second. Looking through how to bridge this gap is an important problem.

State of Cryptocurrency Scaling

This article sums up the present state of cryptocurrency scale and then proposes some different solutions that are being actively explored.

What is the Lightning Network and how can it help

The Lightning Network is a solution to Bitcoin’s scaling problem. This article talks a little bit about the mechanics behind it and helps explain what exactly a specific solution to a scaling problem (even if it’s off-chain) looks like.

Trade

Cryptocurrencies and blockchain – Flipside Crypto

If you want a guide to how to think about cryptocurrencies and get in the position to trade with them practically, this guide, while behind an email wall and targeted mostly to wealth advisors, can help you with a broad overview. Note that the author of this piece isn’t making investment advice here and is, well, me.

Programming/technical skills

C++/Golang

Code your own blockchain in less than 200 lines of Go! – Medium

This handy tutorial dives into how you can use Golang to quickly get up a prototype blockchain, helping you to understand the basics needed here. It’s a handy way to dive right into the blockchain industry.

Blockchain Tutorial | How To Become A Blockchain Developer

This tutorial from Blockgeeks covers some of the same ground as the tutorial above, but uses JavaScript and goes into a bit more detail.

Ethereum/Solidity

Nine Free Resources to Learn Solidity – code(love)

This listable I created has many of my favorite resources to learn Solidity, the programming language of choice for applications on Ethereum. It’s a handy resource to getting you blockchain jobs.

CryptoZombies – Learn to code games on Ethereum

CryptoZombies is a game that will help you learn Solidity interactively. It remains to this day of my favorite resources to onboard people into the language.

Long-term view on the sector

Most people compare what is happening to the blockchain and cryptocurrency space to the first Internet bubble. There may a ”mania” right. That creates a more sobering long-term view on cryptocurrency and blockchain.

One interesting difference between cryptocurrency startups and regular ones are the difference between being accorded stock options against more liquid tokens. In practice, the CEO, investors, and board of directors have much more control over standard startups and employees.

The retention of employees in standard startups (bad) might look stable compared to crypto startups that give their employees liquid tokens they can cash out immediately.

One of the biggest strengths of the cryptocurrency boom, the pulling-in of a wave of talented employees with above-average compensation, might come to a screeching halt when things turn sour. This is something to keep in mind as you’re looking for blockchain jobs.

As more and more entrepreneurs start trying new crypto startups as a way to raise funding, there will be more talent focused on the sector. However, as crypto prices fluctuate, the sector becomes unstable. Lots of thought needs to be placed on how this affects the broader economy and jobs in cryptocurrency and blockchain.

Job boards

Job boards for cryptocurrency jobs and blockchain jobs are under development: here are some good ones:

The path to blockchain jobs

The way you get blockchain jobs and cryptocurrency jobs is often network or referral-based. You may need to know founders of crypto projects to get in. It’s also possible to get in by applying on AngelList or going to cryptocurrency and blockchain events.