Data Scientist

Evlo AI; Boston (MA); On-site; USD 120,000 - 170,000; Full time

Job summary

Evlo AI is seeking a Data Scientist to turn large datasets into predictive models and decision systems that drive production products and measurable outcomes.

You will work with ML engineers, product managers, and data engineers to translate questions into validated solutions, emphasizing uncertainty and production readiness.

Qualifications

• The candidate has 3–8 years of experience in data science or a related quantitative role with production-ready models.
• Strong Python and SQL skills with hands-on experience in data manipulation and visualization libraries.
• Solid understanding of statistical inference, experimental design, regression, and model evaluation.

Responsibilities

• Design, train, and evaluate classification, regression, ranking, and forecasting models using Python, pandas, scikit-learn, XGBoost, or PyTorch
• Analyze behavior, operational, and product data with SQL and Python to identify trends and opportunities
• Develop reproducible feature engineering and data prep workflows using Spark, dbt, Airflow, or cloud data warehouses
• Run statistically sound experiments, including A/B tests and power analyses
• Deploy models and analytical outputs through production pipelines with ML engineers
• Monitor model performance, data quality, drift, and business impact using dashboards and alerts
• Present findings and recommendations to technical and non-technical stakeholders with clear visuals

Skills

Python

SQL

pandas

NumPy

scikit-learn

Visualization

Education

Bachelor's or Master's in statistics, computer science, mathematics, engineering, economics, or related quantitative discipline

Tools

Spark

dbt

Airflow

Snowflake

BigQuery

Redshift

Databricks

Job description

About The Role

The Data Scientist will turn large, high-dimensional datasets into predictive models, experiments, and decision systems that support production products and measurable business outcomes. The work spans exploratory analysis, feature engineering, statistical modeling, and machine learning across structured and unstructured data.

The role partners with ML engineers, product managers, and data engineers to move from ambiguous questions to validated solutions. Success requires rigorous analysis, clear communication of uncertainty, and practical experience taking models and insights into production.

Key Responsibilities

• Design, train, and evaluate classification, regression, ranking, and forecasting models using Python, pandas, scikit-learn, XGBoost, or PyTorch
• Analyze behavioral, operational, and product data with SQL and Python to identify trends, causal drivers, anomalies, and opportunities for improvement
• Develop reproducible feature engineering and data preparation workflows using tools such as Spark, dbt, Airflow, or cloud data warehouses
• Run statistically sound experiments, including A/B tests, power analyses, metric design, and interpretation of treatment effects
• Deploy models and analytical outputs through production pipelines in partnership with ML engineers, including batch inference, real-time scoring, and model versioning
• Monitor model performance, data quality, drift, and business impact using dashboards, automated checks, and alerting systems
• Present findings, model tradeoffs, and recommendations to technical and non-technical stakeholders through clear documentation and concise visualizations

What We Are Looking For

• 3–8 years of experience in data science, applied statistics, machine learning, or a related quantitative role, including experience delivering models or analyses used in production
• Strong Python and SQL skills, with hands‑on experience using pandas, NumPy, scikit‑learn, and visualization libraries such as matplotlib, Seaborn, or Plotly
• Solid understanding of statistical inference, experimental design, regression, classification, model evaluation, sampling, and uncertainty estimation
• Experience working with cloud data platforms or distributed processing tools such as Snowflake, BigQuery, Redshift, Databricks, or Spark
• Ability to translate ambiguous product or business problems into measurable objectives, analytical plans, and deployable solutions
• Bachelor’s or master’s degree in statistics, computer science, mathematics, engineering, economics, or a related quantitative discipline
• Bonus: Experience with causal inference, recommender systems, NLP or LLM applications, MLOps tooling, Kubernetes, and model serving platforms such as AWS SageMaker, Vertex AI, or MLflow