What are the responsibilities and job description for the Data Scientist position at Evlo AI?
About The Role
The role focuses on leveraging advanced statistical modeling, machine learning, and experimental design to drive product decisions and optimize core algorithms. The team processes large-scale user interaction data to build predictive models that directly influence product features and business strategy.
Working closely with product managers, data engineers, and business stakeholders, this role will translate complex business questions into structured analytical frameworks. The focus is on delivering actionable insights, designing robust A/B tests, and deploying production-grade data pipelines.
Key Responsibilities
The role focuses on leveraging advanced statistical modeling, machine learning, and experimental design to drive product decisions and optimize core algorithms. The team processes large-scale user interaction data to build predictive models that directly influence product features and business strategy.
Working closely with product managers, data engineers, and business stakeholders, this role will translate complex business questions into structured analytical frameworks. The focus is on delivering actionable insights, designing robust A/B tests, and deploying production-grade data pipelines.
Key Responsibilities
- Design, execute, and analyze rigorous A/B tests and multivariate experiments to validate product features and algorithm changes.
- Develop and deploy predictive models and statistical analyses using Python, SQL, and scikit-learn to understand user behavior and optimize engagement.
- Build and maintain automated data pipelines and dashboards in dbt and Tableau to track key performance metrics and model health.
- Collaborate with data engineering teams to define data schemas and instrumentation requirements for new product features.
- Translate complex quantitative findings into clear, actionable recommendations for executive and product leadership.
- Conduct deep-dive exploratory data analyses on high-dimensional datasets to uncover trends, anomalies, and product opportunities.
- 3–6 years of experience as a Data Scientist or Quantitative Analyst, preferably in a fast-paced technology environment.
- Strong proficiency in Python and SQL for data extraction, manipulation, and statistical analysis.
- Solid foundation in statistics, including experimental design (A/B testing), hypothesis testing, regression analysis, and causal inference.
- Experience working with large-scale datasets using technologies like Spark, Snowflake, or BigQuery.
- Bachelor's or Master's degree in a quantitative field such as Statistics, Computer Science, Economics, Mathematics, or equivalent practical experience.
- Bonus: Experience with Bayesian statistics, machine learning deployment workflows, or writing production code in a collaborative git environment.