Data Scientist

Evlo AI; Boston (MA); On-site; USD 150,000 - 210,000; Full time

Job summary

Evlo AI is seeking a data scientist to bridge applied statistics, experimentation, and production ML. You will design experiments, build predictive models, and ship data science work into production in collaboration with ML engineers and product teams.

You will ownA/B testing design, develop scalable data pipelines, and craft dashboards while mentoring junior data scientists and advancing experimentation standards across the team.

Qualifications

• 4–8 years of experience in data science, statistics, or quantitative research.
• Expert-level SQL and strong Python; experience with pandas, PySpark, and ML frameworks.
• Deep understanding of causal inference and experimentation: hypothesis testing and A/B design.
• Track record of shipping models or analyses that influenced product or business outcomes.
• Experience with cloud data platforms and modern orchestration tools.
• MS or PhD in a quantitative field or equivalent practical experience.
• Bonus: uplift modeling, time-series forecasting at scale, or internal experimentation platforms.

Responsibilities

• Design and execute rigorous A/B tests and quasi-experimental analyses, defining metrics, power calculations, and guardrail frameworks
• Build, validate, and deploy predictive models (classification, forecasting, uplift modeling) using Python, scikit-learn, XGBoost, and PyTorch
• Develop and maintain large-scale data pipelines in SQL and PySpark, partnering with data engineering to ensure feature correctness and lineage
• Translate ambiguous business questions into measurable problems, then communicate findings and tradeoffs clearly to technical and non-technical stakeholders
• Own the analytical health of key product surfaces: define success metrics, instrument tracking, and build dashboards in Looker, Tableau, or dbt-backed environments
• Productionize data science work — batch scoring jobs, real-time features, and model monitoring — in collaboration with MLOps engineers on AWS or GCP
• Mentor junior data scientists and contribute to team standards for code review, reproducibility, and experiment methodology

Skills

causal inference

experimental design

SQL

Python

production ML

Education

MS or PhD in a quantitative field

Tools

Looker

Tableau

dbt

scikit-learn

XGBoost

PyTorch

PySpark

Job description

About The Role

The role sits at the intersection of applied statistics, experimentation, and production machine learning — turning messy behavioral and transactional data into models and analyses that directly shape product and business decisions.

You will partner with ML engineers, product managers, and analysts to design experiments, build predictive models, and ship data science work that reaches production rather than stopping at a slide deck.

Key Responsibilities

• Design and execute rigorous A/B tests and quasi-experimental analyses, defining metrics, power calculations, and guardrail frameworks
• Build, validate, and deploy predictive models (classification, forecasting, uplift modeling) using Python, scikit-learn, XGBoost, and PyTorch
• Develop and maintain large-scale data pipelines in SQL and PySpark, partnering with data engineering to ensure feature correctness and lineage
• Translate ambiguous business questions into measurable problems, then communicate findings and tradeoffs clearly to technical and non-technical stakeholders
• Own the analytical health of key product surfaces: define success metrics, instrument tracking, and build dashboards in Looker, Tableau, or dbt-backed environments
• Productionize data science work — batch scoring jobs, real-time features, and model monitoring — in collaboration with MLOps engineers on AWS or GCP
• Mentor junior data scientists and contribute to team standards for code review, reproducibility, and experiment methodology

What We Are Looking For

• 4–8 years of experience in data science, applied statistics, or quantitative research, with at least 2 years in a product or tech environment
• Expert-level SQL and strong Python; experience with pandas, PySpark, and at least one ML framework (scikit-learn, XGBoost, PyTorch)
• Deep understanding of causal inference and experimentation: hypothesis testing, power analysis, difference-in-differences, and A/B test design
• Track record of shipping models or analyses that measurably influenced product or business outcomes — not just notebooks
• Experience with cloud data platforms (Snowflake, BigQuery, Databricks) and modern orchestration tools (Airflow, dbt)
• MS or PhD in a quantitative field (statistics, computer science, economics, operations research) or equivalent practical experience
• Bonus: Experience with uplift modeling, multi-armed bandits, time-series forecasting at scale, or building internal experimentation platforms