synthesia - Commercial Data Scientist
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Requirements
• You’re a pragmatic, commercial-minded data scientist who enjoys owning outcomes — not just analysis. • You can take a fuzzy commercial problem, shape it into something measurable, and ship a solution that keeps working over time. • 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. • 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). • We optimize for responsibility and freedom. • 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.
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.
Benefits
• 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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