Data Scientist

Evlo AI; Minneapolis (MN); On-site; USD 110,000 - 160,000; Full time

Job summary

Evlo AI is seeking a mid-level Data Scientist to bridge statistics, ML, and product decisions. You will own end-to-end projects from problem framing to deployment, building models and experiments that drive measurable impact with engineering and product partners.

The role emphasizes causal inference, A/B testing, and developing robust data pipelines while communicating insights to non-technical stakeholders. A strong foundation in statistics and Python is essential.

Qualifications

• 3–6 years of experience in data science, applied statistics, or ML with shipped models or high-impact analyses.
• Expert SQL and Python; comfort with pandas, NumPy, and at least one workflow orchestration tool (Airflow, dbt).
• Hands-on experience designing and analyzing controlled experiments, including causal inference techniques (diff-in-diff, synthetic control, matching).
• Solid grounding in statistics and ML fundamentals: regularization, cross-validation, feature leakage, bias-variance tradeoff.
• Experience communicating quantitative findings to non-technical stakeholders and influencing product or business decisions.
• MS or PhD in a quantitative field (Statistics, CS, Economics, Engineering) or equivalent industry experience.
• Bonus: Snowflake/BigQuery at scale, causal ML libraries (EconML, DoWhy), LLM-assisted analytics workflows, or prior work in marketplace, fintech, or SaaS domains.

Responsibilities

• Design and analyze A/B tests and quasi-experimental studies, including power analysis, variant design, and statistical significance frameworks
• Build, validate, and iterate on predictive models (classification, forecasting, churn, propensity) using Python, scikit-learn, and gradient boosting libraries (XGBoost, LightGBM)
• Own the transition of models from prototype to production, partnering with ML engineers on deployment, monitoring, and retraining pipelines
• Develop SQL-based data pipelines and feature definitions, working closely with analytics engineers on data quality and metric consistency
• Translate business questions into rigorous analytical frameworks and communicate findings through clear visualizations and stakeholder-ready narratives (Tableau, Plotly, Streamlit)
• Conduct deep-dive analyses to diagnose product and revenue anomalies, identifying root causes and quantifying opportunities
• Contribute to team standards for model documentation, reproducibility, and experiment tracking (MLflow, Weights & Biases)

Skills

Communication

Statistics

Experiment design

Education

MS/PhD in quantitative field

Tools

Python

SQL

Airflow

dbt

Pandas

XGBoost

LightGBM

Job description

About The Role

The role sits at the intersection of applied statistics, machine learning, and product decision-making - turning messy, large-scale data into models and analyses that drive core business decisions.

About The Role

The role sits at the intersection of applied statistics, machine learning, and product decision-making - turning messy, large-scale data into models and analyses that drive core business decisions. As a mid-level Data Scientist, the work spans exploratory analysis, causal inference, and production ML. The role will own projects end-to-end: framing the problem, designing experiments, building models, and shipping measurable impact with engineering and product partners.

Key Responsibilities

• Design and analyze A/B tests and quasi-experimental studies, including power analysis, variant design, and statistical significance frameworks
• Build, validate, and iterate on predictive models (classification, forecasting, churn, propensity) using Python, scikit-learn, and gradient boosting libraries (XGBoost, LightGBM)
• Own the transition of models from prototype to production, partnering with ML engineers on deployment, monitoring, and retraining pipelines
• Develop SQL-based data pipelines and feature definitions, working closely with analytics engineers on data quality and metric consistency
• Translate business questions into rigorous analytical frameworks and communicate findings through clear visualizations and stakeholder-ready narratives (Tableau, Plotly, Streamlit)
• Conduct deep-dive analyses to diagnose product and revenue anomalies, identifying root causes and quantifying opportunities
• Contribute to team standards for model documentation, reproducibility, and experiment tracking (MLflow, Weights & Biases)

What We Are Looking For

• 3–6 years of experience in data science, applied statistics, or machine learning, with a track record of shipped models or high-impact analyses
• Expert-level SQL and strong Python; comfort with pandas, NumPy, and at least one workflow orchestration tool (Airflow, dbt)
• Hands-on experience designing and analyzing controlled experiments, including causal inference techniques (diff-in-diff, synthetic control, matching)
• Solid grounding in statistics and ML fundamentals: regularization, cross-validation, feature leakage, bias-variance tradeoff
• Experience communicating quantitative findings to non-technical stakeholders and influencing product or business decisions
• MS or PhD in a quantitative field (Statistics, Computer Science, Economics, Engineering) or equivalent industry experience
• Bonus: Experience with Snowflake/BigQuery at scale, causal ML libraries (EconML, DoWhy), LLM-assisted analytics workflows, or prior work in marketplace, fintech, or SaaS domains