What are the responsibilities and job description for the Data Scientist position at Evlo AI?
About The Role
The role is responsible for driving the statistical modeling, predictive analytics, and exploratory data analysis that power key business decision systems and automated user experiences. This role translates ambiguous product and operational challenges into rigorous quantitative frameworks, building models that optimize outcomes and surface critical insights.
The data scientist will collaborate closely with machine learning engineers and product managers to scale experimental prototypes into production-grade systems. The ideal candidate balances deep theoretical knowledge of statistical modeling with the pragmatic engineering skills required to manipulate high-volume, complex datasets.
Key Responsibilities
The role is responsible for driving the statistical modeling, predictive analytics, and exploratory data analysis that power key business decision systems and automated user experiences. This role translates ambiguous product and operational challenges into rigorous quantitative frameworks, building models that optimize outcomes and surface critical insights.
The data scientist will collaborate closely with machine learning engineers and product managers to scale experimental prototypes into production-grade systems. The ideal candidate balances deep theoretical knowledge of statistical modeling with the pragmatic engineering skills required to manipulate high-volume, complex datasets.
Key Responsibilities
- Develop, validate, and deploy predictive models using statistical, machine learning, and optimization techniques to solve complex business problems
- Design and execute robust A/B testing frameworks and multi-variate experiments to evaluate product features, measuring long-term impact and causal relationships
- Build and maintain end-to-end data pipelines using SQL, Python, and Spark to transform unstructured and relational data into clean feature stores for modeling
- Collaborate with backend and ML engineering teams to integrate models into production systems and monitor post-deployment performance and drift
- Translate complex analytical and model-based findings into clear, actionable recommendations for cross-functional stakeholders and executive leadership
- Champion data quality, modeling standards, and analytical rigor across the broader engineering and product organizations
- 3–6 years of experience as a Data Scientist or in a quantitative analytical role, with a proven track record of delivering models to production
- Strong proficiency in Python, including libraries such as pandas, NumPy, scikit-learn, and statsmodels, alongside expert-level SQL skills
- Solid understanding of statistical foundations: experimental design, hypothesis testing, regression analysis, and causal inference
- Experience with cloud data platforms and distributed computing frameworks, such as AWS, Snowflake, Databricks, or PySpark
- Master's or Ph.D. in a highly quantitative field (e.g., Statistics, Computer Science, Economics, Mathematics, or Physics)
- Bonus: Experience with deep learning frameworks (PyTorch or TensorFlow), visualization tools (Tableau, Looker), or deploying APIs using FastAPI or Flask