What are the responsibilities and job description for the Data Scientist position at Evlo AI?
About The Role
The role sits at the center of a data platform that powers real product decisions: forecasting demand, scoring risk, detecting anomalies, and surfacing insights that directly change what the business ships next.
You will work with a small, senior team of ML engineers and data engineers, translating messy, high-volume data into models and analyses that hold up under production scrutiny - not just in a notebook.
Key Responsibilities
The role sits at the center of a data platform that powers real product decisions: forecasting demand, scoring risk, detecting anomalies, and surfacing insights that directly change what the business ships next.
You will work with a small, senior team of ML engineers and data engineers, translating messy, high-volume data into models and analyses that hold up under production scrutiny - not just in a notebook.
Key Responsibilities
- Design, build, and validate predictive models (forecasting, classification, propensity, anomaly detection) using Python, scikit-learn, XGBoost, and PyTorch
- Own exploratory analysis and experimentation: design A/B tests, compute power analyses, and deliver statistically sound conclusions to product and engineering stakeholders
- Build and productionize data pipelines in SQL, PySpark, and Airflow, partnering with data engineering on data quality and lineage
- Develop dashboards and self-serve analytics in Looker, Tableau, or Streamlit that make model outputs actionable across the org
- Collaborate with ML engineers to move models from prototype to production, including feature stores, evaluation harnesses, and drift monitoring
- Communicate findings clearly and honestly - in writing and in stakeholder reviews - including the limitations and tradeoffs of every model you ship
- 3–6 years of hands-on data science experience, including at least one model taken from research to production
- Expert-level SQL and Python; strong pandas/NumPy fluency and comfort with large-scale data (Spark a plus)
- Solid statistical foundation: hypothesis testing, causal inference, experimental design, and time-series methods
- Experience with cloud data stacks (Snowflake, Databricks, BigQuery, or Redshift) and workflow tooling like Airflow or dbt
- MS or PhD in a quantitative field (Statistics, Computer Science, Math, Economics) or equivalent practical experience
- Track record of communicating technical results to non-technical stakeholders and influencing product decisions
- Bonus: experience with LLM applications, causal ML ( uplift modeling, doubly robust estimation ), or MLOps tooling such as MLflow and SageMaker