Overview




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.

 

Don’t have a ton of knowledge about GitLab yet? Don’t worry. We have an extensive onboarding and training program at GitLab and you will be provided with necessary DevOps and GitLab knowledge to fulfil your role.

 

What you’ll do in this role:

  • 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, behavioural 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 the handbook first!

 

We’re looking for:

  • Ability to use GitLab
  • 4+ years of professional experience in an analytics role
  • 2+ years of professional experience in 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, sci-kit-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
  • A shared interest in our values, and working in accordance with those values

Also, we know it’s tough, but please try to avoid the ​​confidence gap​.​​ You don’t have to match all the listed requirements exactly to be considered for this role.

 

Hiring Process

To view the full job description and hiring process, please view our​ ​handbook​. Additional details about our process can also be found on our ​hiring page​.