Data Scientist, Enterprise Analytics

Description

The company makes a platform for DevSecOps that helps organizations raise developer productivity, run operations more efficiently, lower security and compliance risk, and speed up digital transformation.

More than 50 million people are registered users, and more than 50% of the Fortune 100 rely on it to ship secure software faster. Fortune 100 here means the top 20% ranked companies in the 2025 Fortune 500 list, published June 2025, and the figures come from company data.

The team expects every member to use AI in daily work to boost efficiency, innovation and impact. The culture is high-performance and values-driven, with continuous knowledge sharing and work on complex problems alongside industry leaders.

You will join Enterprise Analytics, which builds the trusted data products and insights behind company-wide sales, revenue and finance reporting. The team tracks metrics such as First Orders, pipeline, CRR, ARR and other key indicators so leaders can decide well and teams can see how their work adds to growth.

You will be a trusted analytical partner to Sales, Revenue Operations and Finance. You will help define how go-to-market performance is measured and where to invest for growth. The role is strategic and hands-on, using statistics, experimentation and machine learning to forecast outcomes, find what drives activation, conversion, expansion and retention, and size opportunities. You will also support the company-level GTM reporting that executives use, covering consumption, ARR and first orders.

Responsibilities

• Work closely with business teams as a trusted partner who turns data into insight and supports strategic decisions across go-to-market.
• Explore data to find behavioral trends, long-term patterns, and the main drivers and levers of business performance, then explain the results clearly to non-technical audiences.
• Build machine learning models, such as propensity, product-qualified lead (PQL) scoring, churn and expansion models, covering training, validation, testing and feature engineering. Take them to production with MLOps practices: CI/CD for ML pipelines, model registries, automated retraining and monitoring.
• Design, run and assess growth experiments, including A/B tests and quasi-experiments, on onboarding, pricing and packaging, and upgrade flows. Measure their effect on activation, conversion and retention.
• Build and run agentic workflows for analysis, modeling and pipeline automation. Add evaluation and monitoring so agent-generated insights stay accurate, traceable and trustworthy.
• Help build the company's semantic layer so stakeholders and agents can self-serve metrics from trusted, tested sources.
• Write production-grade Python and SQL that meets internal standards for style, maintainability and performance in a high-scale database environment. Uphold those standards through code review.

Requirements

• 3-5 years of professional experience in GTM analytics, data science or a similar technical role.
• Ability to turn ambiguous business problems into clear, measurable questions and explain technical ideas to non-technical stakeholders.
• Experience with experimentation and causal inference, including A/B testing, power analysis and quasi-experimental methods, plus statistical and machine learning methods such as regression, classification, clustering and survival analysis.
• Deep hands-on SQL skills in cloud data warehouses such as Snowflake SQL, including product usage and event data, and experience with business intelligence tools such as Looker, Tableau, Hex or similar.
• Experience using Python for statistical modeling, machine learning and production data science, with hands-on work in pandas, NumPy, scikit-learn, Jupyter and related libraries such as LightGBM and XGBoost.
• Hands-on experience building agentic workflows with AI coding agents such as Claude Code, OpenCode or similar. This includes connecting agents to the right context, tools and platforms such as MLflow, and checking their output for accuracy and reliability.
• Experience in a B2C company working with Consumption, CRR, ARR, and Churn & Contraction metrics and motions.
• Self-motivated and self-managing, with strong organizational skills.
• Ability to use the company's product and to work well in a fully remote organization.

Conditions

The role is fully remote. Some roles carry location-based eligibility requirements, and the Talent Acquisition team can answer location questions once the recruiting process starts.

The company hires in countries around the world.

The base salary range for this level is $86,400 - $176,400 USD and currently applies only to residents of the United States. It reflects base pay across US locations and is set by interviews, experience, skills, internal equity, market data and location. It excludes bonuses, equity and benefits.

Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary.

The company welcomes candidates with varied experience, and many successful hires do not meet every requirement.

Benefits

• Flexible paid time off.
• Team member resource groups.
• Equity compensation and an employee stock purchase plan.
• A growth and development fund.
• Parental leave.
• Health, financial and well-being benefits.