The Data Science program at GitLab focuses on developing model-based insights to help us understand our business, customers, and product better. Data Scientists work across the entire development lifecycle, from inception to final delivery. As a result of helping GitLab understand major trends across our business, Data Scientists make significant strategic contributions to new and existing business initiatives.
Data Scientists work with peers on the Data Team and functional teams to:
- perform ad-hoc exploratory analysis
- solve well-defined business problems
- regularly measure and improve analytics initiatives
- create and maintain production models and related applications
Example Data Science projects include:
- account scoring
- propensity to buy
- customer segmentation
- sentiment analysis
- customer churn and uplift prediction
- hypothesis testing and forecasting
Data Scientists are a part of the Data Team and report to the Director/ Sr. Director, Data & Analytics.
Levels
Read more about levels at GitLab here.
Data Scientists (Intermediate) share the same requirements and responsibilities outlined above.
The Data Scientist (Intermediate) is a grade 6.
- Communicate with business partners to understand their needs to help develop new strategic insights
- Define, collaborate, and communicate key influences, levers, and impacts to non-technical audiences
- Perform exploratory data analysis to understand ecosystems, behavioral trends, and long-term trends
- Build machine learning models (training, validation, and testing) with appropriate solutions for data reduction, sampling, feature selection, and feature engineering
- Design and evaluate experiments (including hypothesis testing) by creating key data sets
- Apply data mining or NLP techniques to cleanse and prepare large data sets
- Help grow the Data Science function by defining and socializing best practices, particularly within a DataOps and MLOps data ecosystem
- Craft code that meets our internal standards for style, maintainability, and best practices for a high-scale database environment. Maintain and advocate for these standards through code review
- Document every action in either issue/MR templates, the handbook, or READMEs so your learnings turn into repeatable actions and then into automation following the GitLab tradition of handbook first!
- Ability to use GitLab
- 4+ years professional experience in an analytics role
- 2+ years professional experience in a predictive analytics, data science, or similar role
- Developed 2 or more automated machine learning models for production use
- Developed and presented 4 or more predictive analytical projects
- Familiarity with the CRISP-DM analytics development model
- Experience working with a variety of statistical and machine learning methods (time series analysis, regression, classification, clustering, survival analysis, etc)
- Professional experience with python, including python data libraries (numpy, pandas, matplotlib, scikit-learn), or R
- Deep understanding of SQL in data warehouses (we use Snowflake SQL) and in business intelligence tools (we use Sisense for Cloud Data Teams)
- Working knowledge of statistics
- Comfort working in a highly agile, intensely iterative environment
- Positive and solution-oriented mindset
- Effective communication skills: Regularly achieve consensus with peers, and clear status updates
- Experience owning a project from concept to production, including proposal, discussion, and final delivery
- Self-motivated and self-managing, with excellent organizational skills
- Share our values, and work in accordance with those values
- Ability to thrive in a fully remote organization
- Successful completion of a background check – see the GitLab Code of Business Conduct and Ethics.
- Number of Models Operationalized
- Number of Strategic Insights Discovered (Data Value Calculator score 2 or higher)
- Business Partner Customer Satisfaction Score (CSAT)
- Merge Request Rate
Career Ladder
The next step in the Data Scientist job family is to move to Senior Data Scientist.
Senior Data Scientist
Job Grade
The Senior Data Scientist is a grade 7.
Responsibilities
The Senior Data Scientist has all of the responsibilities of an Intermediate Data Scientist, plus:
- Improve predictive models with data from multiple models
- Automate feedback loops for algorithms/models in production
- Create repeatable processes and scalable data products
- Influence functional teams and develop best practices across the organization
- Review, scale, and enhance operationalized statistical models and algorithms
Requirements
The Senior Data Scientist meets all of the requirements of an Intermediate Data Scientist, plus:
- 6+ years professional experience in an analytics role
- 4+ years professional experience in a predictive analytics, data science, or similar role
- Developed 4 or more automated machine learning models for production use
- Developed and presented 6 or more predictive analytical projects
- Developed communication skills with ability to explain statistic and mathematical concepts to non-experts
- Extensive knowledge, application, and experience in creating and implementing recommendation systems, machine learning, NLP, statistics, and deep learning
- Ability to quantify improvements from business efficiency or customer experience based on research outcomes
- Number of Models Operationalized
- Number of Strategic Insights Discovered (Data Value Calculator score 3 or higher)
- Business Partner Customer Satisfaction Score (CSAT)
- Merge Request Rate
Hiring Process
Candidates for this position can expect the hiring process to follow the order below. Please keep in mind that candidates can be declined from the position at any stage of the process.
- Selected candidates will be invited to fill out a short questionnaire.
- Qualified candidates will be invited to schedule a 30 minute screening call with one of our Global Recruiters.
- Next, candidates will be invited to schedule a first interview with our Senior Director, Data and Analytics
- Next, candidates will be invited to schedule a second interview with a member from our Data team
- Next, candidates will be invited to schedule a third interview with the business division DRI
- Next, candidates will be invited to schedule a fourth interview with a member from our Data team
Additional details about our process can be found on our hiring page.
Compensation Calculator
To find out more about the compensation for this role, please
apply to a role
first. Once selected for a screening call, you'll be able to sign up here to view our
compensation calculator.
Be sure to use the same email address for both.
About GitLab
GitLab Inc. is a company based on the GitLab open-source project. GitLab is
a community project to which over 2,200 people worldwide have contributed.
We are an active participant in this community, trying to serve its needs
and lead by example. We have one vision: everyone can
contribute to all digital content, and our mission is to change all creative
work from read-only to read-write so that everyone can contribute.
We value results, transparency, sharing, freedom,
efficiency, self-learning, frugality, collaboration, directness, kindness, diversity, inclusion and belonging,
boring solutions, and quirkiness. If these values match your personality,
work ethic, and personal goals, we encourage you to visit our
primer to learn more. Open source is our culture, our way of
life, our story, and what makes us truly unique.
Top 10 Reasons to Work for GitLab:
- Mission: Everyone can contribute
- Results: Fast growth, ambitious vision
- Flexible Work Hours: Plan your day so you are there for other people & have time for personal interests
- Transparency: Over 2,000 webpages in GitLab handbook, GitLab Unfiltered YouTube channel
- Iteration: Empower people to be effective & have an impact, Merge Request rate, We dogfood our own product, Directly responsible individuals
- Diversity, Inclusion & Belonging: A focus on gender parity,
Team Member Resource Groups, other initiatives
- Collaboration: Kindness, saying thanks, intentionally organize informal communication, no ego
- Total Rewards: Competitive market rates for compensation, Equity compensation, global benefits (inclusive of office equipment)
- Work/Life Harmony: Flexible workday, Friends and Family days
- Remote Done Right: One of the world's largest all-remote companies, prolific inventor of remote best practices
See our culture page for more!
Work remotely from anywhere in the world. Curious to see what that looks
like? Check out our remote manifesto and guides.
