Data Scientist

Evlo AI; San Diego (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Evlo AI is seeking a data scientist to translate complex behavioral and operational data into actionable product insights. You will own analyses end-to-end, collaborating with product and engineering to deploy models and monitor performance in production.

Ideal candidates have 3–6 years of experience, strong SQL and Python skills, and a track record of shipped analyses. The role involves building reproducible pipelines and dashboards to support decision-making in a fast-paced environment.

Qualifications

• 3–6 years of experience in data science, applied statistics, or ML with production impact
• Strong SQL and Python proficiency for production analyses
• Experience designing and analyzing A/B tests and causal inference analyses

Responsibilities

• Design, build, and validate statistical and ML models that ship to production
• Run rigorous A/B test designs and causal inference analyses
• Build reproducible data pipelines in Python and SQL with data engineering collaboration
• Develop dashboards in Looker, Tableau, or Hex used for decision making
• Translate business questions into analytical problems and communicate findings clearly
• Deploy models via APIs or batch jobs with monitoring for drift
• Contribute to team standards for experimentation and model documentation

Skills

Advanced SQL

Python (pandas, scikit-learn)

Statistics & experiments

A/B testing design

Communication

Production ML deployment

Education

MS or PhD in quantitative field

Tools

SQL

Python

PySpark

Airflow

dbt

Looker

Tableau

Hex

Job description

About The Role

The role focuses on turning complex, high-volume behavioral and operational data into models that drive core product decisions — forecasting, anomaly detection, segmentation, and causal inference problems where the answer changes what the business does next.

You will work within a small, senior data science team embedded directly with product and engineering, owning analyses and models end-to-end from exploratory work through production deployment and measurement.

Key Responsibilities

• Design, build, and validate statistical and ML models — forecasting, classification, uplift, and anomaly detection — that ship to production and influence product and pricing decisions
• Run rigorous A/B test design and causal inference analyses (diff-in-diff, synthetic control, instrumental variables) on experiments with ambiguous or noisy data
• Build reproducible data pipelines in Python and SQL, partnering with data engineering to keep feature tables reliable and performant at scale
• Develop and maintain dashboards and self-serve analytics in tools like Looker, Tableau, or Hex that stakeholders actually use to make decisions
• Translate ambiguous business questions into well-scoped analytical problems, and communicate findings clearly to technical and non-technical audiences
• Deploy models via APIs or batch jobs using Airflow, dbt, and cloud infrastructure (AWS or GCP), with monitoring for drift and performance degradation
• Contribute to team standards for experimentation methodology, model documentation, and code review

What We Are Looking For

• 3–6 years of experience in data science, applied statistics, or ML, with a track record of models or analyses that shipped to production
• Expert-level SQL and strong Python (pandas, scikit-learn, statsmodels; PySpark a plus)
• Deep grounding in statistics: hypothesis testing, causal inference, regularization, and knowing when a simple baseline beats a complex model
• Hands-on experience designing and analyzing A/B tests, including power analysis and sequential testing pitfalls
• Experience working with large, messy datasets and the judgment to know when data quality issues invalidate results
• Strong communication skills — able to present methodology, uncertainty, and tradeoffs to senior stakeholders without overselling
• MS or PhD in a quantitative field (Statistics, CS, Economics, Operations Research) or equivalent practical experience. Bonus: experience with causal ML libraries (EconML, DoWhy), dbt/Airflow pipelines, or prior work in marketplace, fintech, or SaaS domains.