What are the responsibilities and job description for the Data Scientist position at Evlo AI?
About The Role
The role is for a hands-on data scientist who turns messy, large-scale behavioral and transactional data into models that drive real product and business decisions - not dashboards that nobody reads.
Working within a cross-functional squad of ML engineers and product owners, the data scientist owns the full analytical lifecycle: framing ambiguous problems, building predictive models, running rigorous experiments, and shipping insights that survive stakeholder scrutiny.
Key Responsibilities
The role is for a hands-on data scientist who turns messy, large-scale behavioral and transactional data into models that drive real product and business decisions - not dashboards that nobody reads.
Working within a cross-functional squad of ML engineers and product owners, the data scientist owns the full analytical lifecycle: framing ambiguous problems, building predictive models, running rigorous experiments, and shipping insights that survive stakeholder scrutiny.
Key Responsibilities
- Design, build, and productionize predictive models (forecasting, churn, propensity, ranking) using Python, scikit-learn, and PyTorch
- Develop and maintain feature pipelines in SQL and PySpark against petabyte-scale data warehouses (Snowflake, Databricks)
- Design and analyze A/B tests and quasi-experiments; communicate statistical findings with clear confidence levels and business impact framing
- Partner with ML engineers to deploy models via containerized services (Docker, AWS) and define monitoring for drift and performance degradation
- Build reusable analytical tooling and dashboards in dbt, Airflow, and Tableau that reduce one-off analysis work across the org
- Translate ambiguous product questions into concrete analytical plans, present results to senior stakeholders, and defend methodology under pressure
- 3–6 years of experience in data science, applied statistics, or ML, with models or analyses that shipped to production
- Expert-level SQL and strong Python (pandas, NumPy, scikit-learn); comfort working with large datasets in Spark or similar
- Solid grounding in statistics: hypothesis testing, causal inference, regression, time-series methods, and their real-world failure modes
- Experience designing and analyzing controlled experiments (A/B tests) at scale
- Track record of communicating technical findings to non-technical stakeholders through clear writing and presentation
- MS or PhD in a quantitative field (Statistics, CS, Economics, Physics, Applied Math) or equivalent industry experience
- Bonus: experience with LLM-assisted workflows, causal inference libraries (DoWhy, EconML), dbt, or prior work in marketplace/subscription businesses