What are the responsibilities and job description for the Data Scientist position at Evlo AI?
About The Role
The Data Scientist will develop, validate, and deploy statistical and machine learning models that turn high-volume product and operational data into measurable business outcomes. The work spans exploratory analysis, feature engineering, experimentation, forecasting, and predictive modeling using Python, SQL, and modern cloud data platforms.
The role partners closely with product managers, software engineers, and ML engineers to define success metrics, translate ambiguous questions into rigorous analyses, and move models from notebooks into reliable production workflows. The team values clear technical communication, reproducible analysis, and models that perform against real-world constraints—not just offline benchmarks.
Key Responsibilities
The Data Scientist will develop, validate, and deploy statistical and machine learning models that turn high-volume product and operational data into measurable business outcomes. The work spans exploratory analysis, feature engineering, experimentation, forecasting, and predictive modeling using Python, SQL, and modern cloud data platforms.
The role partners closely with product managers, software engineers, and ML engineers to define success metrics, translate ambiguous questions into rigorous analyses, and move models from notebooks into reliable production workflows. The team values clear technical communication, reproducible analysis, and models that perform against real-world constraints—not just offline benchmarks.
Key Responsibilities
- Build and evaluate predictive models for classification, regression, ranking, forecasting, and anomaly detection using Python, pandas, scikit-learn, and gradient-boosting frameworks
- Analyze large, complex datasets with SQL and Python to identify product trends, customer behavior patterns, operational risks, and opportunities for improvement
- Design and measure A/B tests and quasi-experimental analyses, defining appropriate success metrics, guardrails, sample sizes, and statistical significance thresholds
- Develop feature engineering and data validation workflows using tools such as dbt, Spark, Airflow, and cloud data warehouses including Snowflake or BigQuery
- Translate analytical findings into clear recommendations, dashboards, and decision-ready presentations for technical and non-technical stakeholders
- Collaborate with ML engineers to productionize models through APIs or batch pipelines, including model versioning, monitoring, retraining, and performance tracking
- Document assumptions, methodology, limitations, and results; review analytical work and contribute to team standards for reproducibility and model governance
- 3–6 years of experience in data science, applied statistics, machine learning, or a closely related discipline, including experience delivering analyses or models used in production
- Strong Python and SQL skills, with hands-on experience using pandas, NumPy, scikit-learn, and notebooks or equivalent development environments
- Solid understanding of statistical inference, experimental design, regression, classification, model evaluation, and common sources of bias and data leakage
- Experience working with large-scale data in a cloud environment and familiarity with modern warehouses or processing systems such as Snowflake, BigQuery, Databricks, or Spark
- Demonstrated ability to define business or product questions, select appropriate analytical methods, and communicate findings clearly to both technical and non-technical audiences
- Experience deploying or partnering on deployment of machine learning models, including monitoring data quality, model drift, latency, and production performance
- Bonus: Experience with causal inference, time-series forecasting, dbt, Airflow, MLflow, Docker, cloud platforms such as AWS or GCP, or applied work with NLP and generative AI systems