What are the responsibilities and job description for the Data Scientist position at Evlo AI?
About The Role
The Data Scientist will turn large, complex datasets into production-grade models and decision systems across forecasting, experimentation, personalization, and customer-facing analytics. The role covers the full lifecycle: defining measurable objectives, exploring data, building models, validating results, and partnering with engineering to deploy and monitor them.
The team works with structured and unstructured data using Python, SQL, Spark, and modern machine learning frameworks. This role matters because model quality must translate into measurable product and business outcomes, with clear attention to experimental rigor, data quality, scalability, and responsible use of machine learning.
Key Responsibilities
The Data Scientist will turn large, complex datasets into production-grade models and decision systems across forecasting, experimentation, personalization, and customer-facing analytics. The role covers the full lifecycle: defining measurable objectives, exploring data, building models, validating results, and partnering with engineering to deploy and monitor them.
The team works with structured and unstructured data using Python, SQL, Spark, and modern machine learning frameworks. This role matters because model quality must translate into measurable product and business outcomes, with clear attention to experimental rigor, data quality, scalability, and responsible use of machine learning.
Key Responsibilities
- Develop and validate statistical and machine learning models for forecasting, ranking, classification, segmentation, and anomaly detection using Python, pandas, scikit-learn, XGBoost, or PyTorch
- Analyze large datasets with SQL and Spark to identify behavioral patterns, data quality issues, and opportunities for product or operational improvement
- Design and evaluate A/B tests, causal analyses, and offline benchmarks; define success metrics and communicate confidence, tradeoffs, and limitations clearly
- Build reproducible training and feature pipelines with tools such as Airflow, dbt, MLflow, or equivalent orchestration and experiment-tracking systems
- Partner with software and platform engineers to deploy models through batch or real-time services on AWS, GCP, or Azure
- Monitor production models for drift, data integrity, latency, calibration, and performance regression; establish alerting and retraining criteria
- Present technical findings to product, engineering, and business stakeholders through concise analysis, dashboards, and written recommendations
- 3–8 years of experience in data science, applied machine learning, quantitative analytics, or a closely related field, including experience delivering models or analyses used in production
- Advanced Python and SQL skills, with hands-on experience using pandas, NumPy, scikit-learn, and either Spark, PyTorch, or TensorFlow
- Strong understanding of statistical inference, experimental design, regression, classification, model evaluation, feature engineering, and uncertainty estimation
- Experience working with cloud data and ML platforms such as AWS, GCP, or Azure, including data warehouses, object storage, and production model workflows
- Demonstrated ability to translate ambiguous product or business questions into measurable objectives, analytical plans, and actionable recommendations
- Bachelor’s or master’s degree in computer science, statistics, mathematics, data science, engineering, economics, or a related quantitative discipline
- Bonus: Experience with LLM evaluation, recommendation systems, causal inference, time-series forecasting, MLflow, dbt, Airflow, Docker, Kubernetes, or responsible AI practices