Everything You Need to Write a Data Scientist Job Description
When people say “big data,” they aren’t kidding.
Every day, the human race creates 2.5 quintillion bytes of data. More data has been generated in the last 10 years alone than in the entirety of human history.
From world-changing arenas like healthcare and human rights to more day-to-day functions like sales and marketing, that data can inform our decisions and guide our actions, but only if we’re able to effectively manage it.
That’s where Data Scientists come in.
Companies around the world are hiring Data Scientists in droves, but there aren’t many candidates to go around so competition is fierce. The first step to hiring a great Data Scientist is writing a great Data Scientist job description, which brings us here today. We’ll walk you through what it is a Data Scientist does, provide some sample job descriptions and a template and even cover some salary information.
Read on or jump ahead to the information you’re looking for.
Table of Contents
- What Does a Data Scientist Do?
- Five Data Scientist Job Description Examples
- Data Scientist Job Description Template
- Data Scientist Salary Information
What Does A Data Scientist Do?
You can’t write a Data Scientist job description if you don’t know what they do, so let’s cover the basics before we go any further.
At the highest level, Data Scientists employ a mix of statistical and technical methodologies to extract meaning from the unbelievable volume of data we create every day. These insights are typically used to inform models and algorithms that power everything from sales pipelines to new product development.
Imagine you have a database that contains the purchasing behavior of every customer that’s done business with your company over the past 10 years. This information is a potential goldmine of insight. It can tell you what products to advertise to specific customers. It can tell you how to manage your inventory. It can even identify products you need to add to or drop from your shelves. The thing is, this database contains millions of individual data points, and gleaning meaningful insights from this information is impossible without a highly structured and automated process. This is why Data Scientists make the big bucks.
Data Scientists blend a mastery of statistics and mathematics with a host of highly technical skills. While requirements vary, most companies expect Data Scientists to bring experience in several key areas to the table:
- Machine Learning - “A field of artificial intelligence that uses statistical techniques to give computer systems the ability to ‘learn’ from data, without being explicitly programmed.”
- Natural Language Processing - “A subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages; in particular how to program computers to process and analyze large amounts of natural language data.”
- Data Mining - “The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.”
- Data Visualization - “To communicate information clearly and efficiently, data visualization uses statistical graphics, plots, information graphics and other tools.”
Data Scientists utilize many different programming languages, but some are more prevalent than others. You should be at least somewhat familiar with the following languages before attempting to write a job description.
- R - R is an open source language for statistical computing and data visualization. Its roots go back to 1976, and it remains one of the most utilized languages in data science.
- Python - Python is a general-purpose programming language popular with Data Scientists thanks to its simplicity and efficiency.
- SQL - SQL, short for Structured Query Language, allows programmers to access and manipulate databases.
While the work of a Data Scientist is highly technical, there are also a host of softer skills that are important for success.
Data Scientists are often handed messy, unstructured data sets and asked to derive insights, so look for candidates with a tenacious attitude and plenty of patience. Data Scientists are also tasked with presenting highly technical information to a non-technical audience (often at the senior level), so the ability to communicate clearly and speak with confidence is paramount.
Five Data Scientist Job Description Examples
Let’s take a look at a few real examples of Data Scientist job descriptions that we sourced from our seven online communities. Though these are real job descriptions, we have redacted some information to protect the privacy of the companies that originally posted them.
Data Scientist Job Description: Example 1
At [Redacted], every dataset tells a story. Do you have what it takes to decipher the clues?
We’re looking for a data-savvy individual to join our team as a data scientist, to support data-centric product development and create consumer-focused research content based on our real-time feed of billions of [Redacted] search results along with an archive of several trillion data points.
You may be a great fit for our team if you are excited about exploring huge (and sometimes messy) data sets and finding effective ways to simplify and communicate the results to a non-technical audience.
In this role you will:
- Transform complex analyses into short, compelling, and easy to understand studies to share with journalists aimed at a consumer audience
- Frame and conduct complex analyses and experiments using tremendously large (e.g. 10^6 to 10^10 records), complex (not always well-structured, highly variable) data sets
- Design and implement ad hoc and automated analysis scripts, design and deliver appropriate summary tables, charts and interactive tools to present your results
A perfect candidate has:
- A degree in Math, Statistics, Computer Science, Engineering or other quantitative discipline
- Extremely strong analytical and problem-solving skills
- Proven ability to communicate complex technical work to a non-technical audience
- A strong passion for and extensive experience in conducting empirical research and answering hard questions with data
- Experience with relational databases and SQL, especially Hive
- Experience working with extremely large data sets
- Experience in Pandas, R, SAS or other tools appropriate for large scale data preparation and analysis
- Experience with data mining, machine learning, statistical modeling tools and underlying algorithms
- Proficiency with Unix/Linux environments
Sound like a fit? We can't wait to hear from you.
Data Scientist Job Description: Example 2
As a Data Scientist at [Redacted], you are free to explore unique solutions and try fresh ideas that will benefit our trading business and investment companies. You’ll collaborate with some of tech’s and analytics’ sharpest minds to solve the firm’s ever-changing but exciting challenges. Working in tech at [Redacted] means that you’ll always be presented with a variety of new possibilities as you continue to enhance your skills.
You’ll be responsible for:
- Converting human pattern recognition and decision-making processes into automated aspects of the trading workflow.
- Finding innovative solutions to known problems that will support and expand our core business.
- Driving the firm forward with new ideas that will impact the way we leverage market data.
- Defining best practices within our analytics team regarding prototyping and scaling solutions.
- Assisting software developers in translating research results to production.
- Mentoring junior researchers on the analytics and trading teams.
While you’ll gain experience in this role, you should already have:
- Five years of experience as a researcher or machine learning engineer.
- A demonstrated track record of applying ML techniques to real-world problems.
- Extensive knowledge of statistical modeling.
- An interest in staying up-to-date with recent advances in machine learning.
- The ability to collaborate with subject matter experts in defining appropriate features and metrics for a given model.
- The confidence to take the lead on a project, work through the practical business decisions involved, and carry it to completion in a timely manner.
- Comfort implementing new models from papers.
- Experience working with large, noisy, or incomplete datasets.
- Expertise in Python (Pytorch and Tensorflow preferred) and SQL.
Data Scientist Job Description: Example 3
As a Data Scientist, you will implement machine learning models into critical business processes, forecast industry trends, and build data pipelines. The Data Science team works closely with engineers, pricing analysts, and the business intelligence team to complement qualitative knowledge of the freight industry with rigor and quantitative insights. We’re currently living in AWS infrastructure including SageMaker and Redshift. You will be expected to autonomously run your own projects, drive business impact, and be a domain expert for teams across the company.
What you'll do:
- Develop and implement pricing strategies into our automated bidding processes
- Build forecasting models for freight market pricing changes using industry indicators
- Collaborate with our Sales, Operations, and Tech teams to provide analytic and machine learning support
- Execute long-term modeling initiatives
- Drive adoption of improved, innovative technologies and tools
- Act as a mentor for the data science team
- Help drive strategic data-driven analyses, insights, and report on KPIs
- Partner with data engineering for system design and infrastructure
- You have a Bachelor’s degree in a STEM or quantitative field
- You love data and have at least 2+ years experience with conducting in-depth data analyses, building machine learning models, and presenting results to business stakeholders
- You have a solid grasp on both theory and application, and you know how to balance methodological purity with practical implementation
- You are fluent with programming languages (Python, R)
- You are comfortable working in high-paced, impact-driven environments
- Price optimization and financial forecasting knowledge is a plus
Data Scientist Job Description: Example 4
Data science is the core around which [Redacted] is built. The central value proposition of our product is that we provide e-commerce businesses the ability to:
- organize the overwhelming amount of data they deal with
- analyze it for patterns and predictions
- deliver these insights in a readily accessible, freely explorable way
- and do it again, repeatedly and reliably, as more data comes in the door
The data science team at [Redacted] can be found hard at work at each step above. Our projects range from the development and refinement of statistical models, to the design and implementation of scalable back-end infrastructure that powers our analyses, to collaboration with other teams at [Redacted] to make sure customers get the most out of our analyses.
We place particular emphasis on providing a robust system of statistical insights. Our goal is not to deliver a single paper or presentation’s worth of material, but to give our customers a powerful analytical toolbox. We can help them use it, but we also want them to be able to take it and run with it, exploring their data on their own and finding uses for our models that we hadn’t even thought of.
On top of that, we have an expanding customer base and a growing interest in developing analyses and statistical modeling pipelines that extend beyond e-commerce. With more customers, richer data sets, and new domains of exploration, we’ll face bigger and bigger challenges, but we think we’re up for it, and we are looking for people who want to join us for the ride.
Specifically, we are looking for:
- A solid foundation in statistics, either demonstrated through academic study or work experience.
- Experience with at least two programming languages and one general-purpose statistical package or programming library. The data science team mainly uses R, Scala, and Ruby, but that’s not set in stone, and we value the ability to learn new libraries and ecosystems.
- Friendly and ready to work with others, both technical and non-technical. We don’t work in isolation and regularly interact with each other and members of other teams.
- At least some familiarity with: Unix-like operating systems, SQL databases, and web application programming. Strong experience in any is a plus.
- Other pluses: experience with distributed-computing ecosystems like Spark; experience with Amazon Web Services; experience teaching statistics or programming
Data Scientist Job Description: Example 5
We are looking for a teammate that is excited to take ownership of our data infrastructure and put us on a path where we are iterating quickly and using data to solve some of the biggest problems facing our industry. You will have a seat at the table in making technology decisions and in helping determine what and how we build things as opposed to just getting handed specifications to implement.
We’re looking for someone who is eager to disrupt an extremely archaic industry, but also enjoys startup perks, happy hours, and afternoon ping pong showdowns. We’re a small but quickly growing organization, so every single person here has a mission critical impact on our business.
- Take ownership and lead product development for our internal data processing pipeline
- Perform analysis and generate models to improve our automated data extraction capabilities
- Prototype, test and build models that will be adapted for use in our production data pipeline
- Work closely with the engineering team to advise on system architecture and help guide engineering priorities
- Build and lead a team of analysts and data scientists
- Deepen our culture of data driven decision making
- 18+ months of NLP, Data Science, ML professional work
- Experience specifying and building clean and functional data pipelines
- Comfort manipulating and analyzing complex, unstructured, data from various sources
- Ability to communicate complex quantitative analysis and approaches clearly
Experience with the following would be awesome
- Named Entity Recognition
- Dependency Parsing
- Semantic Role Labeling
- Probabilistic String Matching
- Elasticsearch, Apache Spark
Data Scientist Job Description Template
Every job description you write should accurately reflect the culture and needs of your company, but starting from a template can make life much easier. Looking at the above examples, we can extrapolate some common traits that companies are looking for when hiring Data Scientists and use these to create a Data Scientist job description template. Please note that this template is intended as a jump off point to get you started. Make sure to customize it to your needs.
[Use this section to provide an attention grabbing overview of your company. Include information about your culture, perks and benefits, career development opportunities and anything else that will get candidates excited about your company.]
At [Company Name], data drives everything we do, and we’re looking for a Data Scientist to help us do even more. You understand that data has the power to affect change, and you’re committed to using that power for good. The good of our customers, the good of the business and the good of the world. This is not a back of the house role - the Data Science team collaborates across the company, so you’ll partner with colleagues in engineering, product development, business intelligence and more. Your insights and recommendations will have a direct impact on the business, so come ready to make a difference!
- Own and lead product development within our data pipeline.
- Modify, optimize and scale our existing Data Science products and solutions.
- Develop and implement new methodologies, algorithms and models that will improve how we leverage our data.
- Extract and manipulate data from our existing data sources (includes analysis and reporting).
- Lead the identification and development of new data sources.
- Work collaboratively with team members across the company (engineering, product, etc.) to translate your findings into solutions.
- Communicate data-driven recommendations in a concise and understandable manner.
- Mentor junior analysts and engineers toward continued career growth.
- BS in Mathematics, Computer Science or related quantitative field.
- 3+ years professional experience in Data Science.
- Deep experience with Excel, R, Python and SQL (experience with Scala and Java preferred, but not required).
- Demonstrable experience with Machine Learning and Natural Language Processing.
- Comprehensive knowledge of predictive analytics, statistical modeling and data mining (experience with data visualization preferred but not required).
- Comfortable working with large/noisy/incomplete data sets.
- Exceptional organizational skills and a tenacious dedication to problem solving.
- The ability to effectively communicate highly technical data to a non-technical audience.
Data Scientist Salary Information
Now that you have everything you need to write a great Data Scientist job description, it’s time to talk turkey. Data Scientists are in short supply, and given the importance of the role, they’re typically well compensated.
We’ve gathered Data Scientist salary information from seven of the country’s most competitive employment markets to help you dial in your offer. You can click each bullet point to visit our local compensation analysis tools and do further research.
- Austin, TX: $110,095
- Boston, MA: $126,833
- Chicago, IL: $119,000
- Colorado: $109,706
- Los Angeles, CA: $122,462
- New York, NY: $134,366
- Seattle, WA: $123,010
Cross Market Average Salary for Data Scientists: $120,788