Data Science

The Data Science program at GitLab focuses on supporting internal teams and 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.

Job Grades

Data Scientist (Intermediate)

Data Scientist (Intermediate) Job Grade

The Data Scientist (Intermediate) is a grade 6.

Data Scientist (Intermediate) Responsibilities

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!

Data Scientist (Intermediate) Requirements

Ability to use GitLab
4+ years professional experience in an analytics role OR 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

Data Scientist (Intermediate) Performance Indicators

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 sources
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 OR 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

Senior Data Scientist Performance Indicators

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

Staff Data Scientist

Job Grade

The Staff Data Scientist is a grade 8.

Responsibilities

The Staff Data Scientist has all of the responsibilities of a Senior Data Scientist, plus:

Focus on strategic business impact through activation of Data Product use cases
Coordinate with a cross-functional group of team members to effect change
Develop a technical data science team vision and strategy
Review, scale, and enhance operationalized statistical models and algorithms
Mentor data scientists and data analysts

Requirements

The Staff Data Scientist meets all of the requirements of a Senior Data Scientist, plus:

5+ years professional experience in a predictive analytics, data science, or similar role
Knowledge and experience automating machine learning models at scale
Expertise solving complex and highly impactful quantitative business problems
Demonstrated proficiency with python (pandas, numpy, etc.), SQL, and cloud environments
Expert understanding of statistics and the math behind data science algorithms
Identify and spearhead new data science initiatives, projects, and collaborations that improve results
Willingness to experiment and to confront the hardest or most complex problems

Staff Data Scientist Performance Indicators

Number of Data Products Released
Number of Use Cases Activated
Measurable $ARR Impact (New Business, Cost Savings, or Efficiency Improvement)

Principal Data Scientist

Job Grade

The Principal Data Scientist is a grade 9.

Responsibilities

The Principal Data Scientist has all of the responsibilities of an Staff Data Scientist, plus:

Assist management with project forecasting, roadmapping, and resourcing
Lead major strategic data science projects and initiatives, spanning 6 months or more
Attain a measurable positive impact on the performance of multiple team members and teams
Regularly participates in the Data Community/Industry outside of GitLab through writing, speaking, and networking

Requirements

The Principal Data Scientist meets all of the requirements of an Staff Data Scientist, plus:

8+ years professional experience in a predictive analytics, data science, or similar role
Experience leading strategic projects and large initiatives spanning multiple teams, people, and roles
Demonstrated track record of forming effective cross-functional partnerships; desire to work collaboratively and in a diverse team
Stays current on the state of data science research, processes, tools, and algorithms
Recognized as a leader in the Data Science space across the company

Principal Data Scientist Performance Indicators

$ARR Impact (New Business, Cost Savings, or Efficiency Improvement)
Number of Business Functions which achieve Level 4 of the Data Capability Model
E-Group Customer Satisfaction Score (CSAT) of 4 or higher

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 an interview with a member from our Data Science team
Next, candidates will be invited to schedule an interview with a member from our Data team
Next, candidates will be invited to schedule an interview with the business division DRI
Next, candidates will be invited to schedule an interview with the Senior Director, Data and Analytics

Specialties

Full Stack Data Scientist

Full Stack Data Scientist Description

Full Stack Data Scientist is a hybrid position between Data Scientist and Analytics Engineer

In order for the Data Science team to scale and be successful, we need to develop and promote skills of Analytics Engineers on the team to deliver predictive models faster and more accurately. Full Stack Data Scientist will perform duties of Data Scientist by building predictive models, but also be responsible for backend to ensure smooth delivery.

The job description of this specialization include all of the resposibilities for the Data Scientist role. In addition, resposibilities for the specialization include:

Collaborate with team members to collect business requirements, define successful analytics outcomes, and design data models to support data science projects
Build trust in all interactions and with Trusted Data Development to expand data science capabilities
Serve as the Directly Responsible Individual for major sections of the Enterprise Dimensional Model and data science models
Design, develop, and extend dbt code to feed and automate predictive models

Full Stack Data Scientist Requirements

The job requirements of this specialization include all of the requirements for the Data Scientist role. In addition, requirements for the specialization include:

Demonstrated experience with one or more of the following business subject areas: marketing, finance, sales, product, customer success, customer support, engineering, or people
2+ years experience designing, implementing, operating, and extending commercial Kimball enterprise dimensional models
2+ years working with a large-scale (1B+ Rows) Data Warehouse, preferably in a cloud environment
2+ years experience building reports and dashboards in a data visualization tool

Additional details about our process can be found on our hiring page.

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, Family and Friends 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.

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