Data Scientist, Product & Growth Analytics

Evlo AI; Austin (TX); On-site; USD 120,000 - 170,000; Full time

Job summary

Evlo AI is seeking a Data Scientist to own the full analytical lifecycle, from framing business questions to shipping models that drive product and pricing decisions at scale.

You will work across a modern data stack (SQL, Python, dbt, cloud warehouse) blending causal inference, experimentation, and applied ML, partnering with product managers and data engineers to turn data into shipped decisions.

Qualifications

• 3–6 years of experience in data science, analytics, or applied statistics.
• Advanced SQL fluency and strong Python skills.
• Experience designing controlled experiments (A/B tests).
• Knowledge of causal inference beyond regression: propensity scores, diff-in-diff.
• Experience with Snowflake/BigQuery/Redshift and BI tools (Looker/Tableau).
• Bachelor's degree in quantitative field or equivalent practical experience.
• Bonus: Master's/PhD; causal ML exposure; MLOps exposure.

Responsibilities

• Design and analyze A/B tests and quasi-experimental studies to measure impact of changes.
• Build causal inference and customer-level propensity/segmentation models in Python.
• Develop and maintain production-ready features and models, move to deployment.
• Write large-scale SQL queries and dbt models for reliable data assets.
• Construct dashboards and metrics frameworks in Looker or Tableau for leaders.
• Translate ambiguous business problems into clear analytical plans and deliverables.
• Present findings and model results to senior stakeholders with tradeoffs.

Skills

SQL proficiency

Python data science

A/B testing

Causal inference

Statistical modeling

Product analytics

Communication with stakeholders

Education

Bachelor's degree in Statistics / Mathematics / CS / Economics

Tools

Snowflake

BigQuery

Redshift

dbt

SageMaker

Vertex AI

Looker

Tableau

Spark

PySpark

Job description

Evlo AI is seeking a Data Scientist to own the full analytical lifecycle, from framing business questions to shipping models that drive product and pricing decisions at scale.

You will work across a modern data stack (SQL, Python, dbt, cloud warehouse) blending causal inference, experimentation, and applied ML, partnering with product managers and data engineers to turn data into shipped decisions.