Data Scientist

What you'll do

• Contribute to specific data science projects and initiatives at Semgrep; discovering each department’s most pressing data problems, and proactively identifying the most critical areas to focus your efforts
• Bring your wide knowledge of data-science approaches to each problem you solve: the first day you might build a dashboard to track Board level metrics for the Engineering team, the second you might apply multivariate regression to identify important product features, the third you might apply active-learning techniques to guide data collection and labeling
• Iteratively tackle problems as a series of experiments, proving the value of your work with proof-of-concept to ever more refined results
• Convince your peers of your conclusions with clear data visualizations and well-reasoned explanation
• Help grow your team through the recruitment and hiring of top data talent

Example projects

• Build a client-facing dashboard showing scan time metrics over time to show how the product is improving
• Work together with Product leadership to identify the correct north-star metrics to measure Product usage and what features to build next
• Partner with the rule-writing team to identify the most impactful rules and languages to focus on in real-time
• Build out cleaned/medallion Silver and Gold tables in our Data Lakehouse for internal engineering and product teams to self-serve their analytics needs
• Build an S3 → Snowflake data pipeline and processing engine to improve Repo contributor count metrics for Billing team
• Build a statistical model that analyzes pseudonymous usage data to recommend the next features built into the Semgrep open-source tool
• Consume infrastructure observation metrics to identify and address potential Semgrep.dev registry outages before they occur
• Recruit varied and disjoint data into a “North Star” metric for the performance of the Semgrep open-source tool over time
• Craft a security-rule-recommendation decision tree, using codebase features like languages, frameworks, code sentiment, and commit-message sentiment, to deliver targeted, high-value static-analysis rules to users

Requirements

• 4+ years of experience in data and strategy fields
• Knowledge of data-science approaches; this may include machine-learning algorithms, optimization methods or symbolic artificial-intelligence, but should also include statistical methods and “good-enough” heuristics — and the taste to know when to use each
• Experience clearly visualizing information and experimental results across the full company stack: Board-level, leadership team, and individual team leads
• Sufficient familiarity with production data-processing pipelines to construct them working together with generalist infrastructure engineers; tools we use include S3, FiveTran, DBT, Snowflake, Metabase, Retool, Sagemaker/JupyterNotebook (Python)
• Aptitude delivering technical projects via rapid iterative development
• Experience working on a small team in a fast-paced environment and are willing to experiment with different approaches before settling on the best and most elegant solution given time constraints
• Excellent, proactive communication, both verbal and written

Compensation

• The estimated starting annual salary range for this position is $163,000 - $204,000 USD
• In addition to base salary, total compensation may include equity, variable compensation, and benefits