You'll work end-to-end: from defining the problem with commercial stakeholders, to building and validating models, to deploying and running them reliably in production with the Data Engineering team.

Typical projects include customer health scores, lead intent scoring, churn/expansion predictors, segmentation, and experimentation frameworks that make those models actionable.

Responsibilities

• Partner with Sales, RevOps, CS and Marketing to translate ambiguous commercial questions into measurable problems and model-ready datasets.
• Build and iterate on predictive and classification models (e.g., health scoring, intent scoring), with rigorous validation, monitoring, and clear success metrics.
• Deploy models into production in collaboration with Data Engineering (batch jobs, pipelines, feature generation, versioning, and observability).
• Maintain and improve existing models: performance monitoring, retraining strategies, drift detection, and reliability.
• Make models usable: deliver clear outputs, documentation, and guidance so commercial teams can act on insights.
• Contribute to a strong DS craft culture: code quality, reproducibility, experimentation discipline, and pragmatic model selection.

Requirements

• Several years of industry experience as a Data Scientist (or similar), building statistical/ML models end-to-end.
• Strong foundations in applied machine learning and statistics, with good judgment about model complexity vs. impact.
• Production mindset: you've worked with deployed models, and understand monitoring, retraining, data quality, and operational constraints.
• Strong SQL and Python skills, with experience in data wrangling and feature engineering.
• Ability to communicate clearly with technical and non-technical partners, including explaining trade-offs and model limitations.
• Comfort operating in a high-autonomy environment: you can plan your work, drive alignment, and ship without being handed tickets.

Nice-to-Haves

• Experience working on commercial / go-to-market problems (rev intelligence, lead scoring, churn, expansion, attribution, forecasting).
• Experience working closely with modern data stacks (Snowflake, dbt, Airflow) and production ML patterns.
• Experience designing model outputs that integrate cleanly into commercial workflows (dashboards, alerts, CRM signals).

How We Work

We optimize for responsibility and freedom.

That means:

• No Jira, no ticket conveyor belt , we run on ownership and a small number of high-impact projects.
• Close collaboration with commercial stakeholders and Data Engineering to ship real outcomes.
• A bias toward pragmatic solutions that can be deployed, monitored, and improved.

Why Join

• Work on problems that sit at the intersection of product usage and commercial outcomes.
• Own impactful, end-to-end projects , from definition to production.
• Join a team that values autonomy, craft, and speed.

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