Data Scientist

Evlo AI; Austin (TX); Presencial; USD 120.000 - 170.000; Tempo integral

Resumo da vaga

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.

Qualificações

• 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.

Responsabilidades

• 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.

Conhecimentos

SQL proficiency

Python data science

A/B testing

Causal inference

Statistical modeling

Product analytics

Communication with stakeholders

Formação

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

Ferramentas

Snowflake

BigQuery

Redshift

dbt

SageMaker

Vertex AI

Looker

Tableau

Spark

PySpark

Descrição da vaga

About The Role

The Data Scientist owns the full analytical lifecycle - from framing ambiguous business questions to shipping models and analyses that drive product and pricing decisions at scale.

About The Role

The Data Scientist owns the full analytical lifecycle - from framing ambiguous business questions to shipping models and analyses that drive product and pricing decisions at scale.

Working across a modern data stack (SQL, Python, dbt, cloud warehouse), the role blends causal inference, experimentation, and applied ML, partnering closely with product managers, ML engineers, and data engineers to turn data into shipped decisions.

Key Responsibilities

• Design and analyze A/B tests and quasi-experimental studies (difference-in-differences, synthetic control) to measure the impact of product and pricing changes
• Build causal inference models and customer-level propensity/segmentation models using Python (scikit-learn, statsmodels) to inform growth and retention strategy
• Develop and maintain production-ready features and models, partnering with ML engineers to move models from notebooks to deployment via SageMaker or Vertex AI
• Write large-scale SQL queries and dbt models against Snowflake/BigQuery to build reliable, self-serve data assets for stakeholders
• Construct dashboards and metrics frameworks in Looker or Tableau that define how leadership tracks core business health
• Translate ambiguous business problems into clear analytical plans, hypotheses, and prioritized deliverables
• Present findings and model results to senior stakeholders, including tradeoffs, confidence levels, and recommended actions

What We Are Looking For

• 3-6 years of experience in data science, analytics, or applied statistics, with at least 2 years at a product-driven tech company
• Advanced SQL fluency and strong Python skills (pandas, scikit-learn, statsmodels); comfort working in Spark/PySpark environments
• Hands-on experience designing and analyzing controlled experiments (A/B tests), including power analysis and variance reduction techniques
• Working knowledge of causal inference methods beyond standard regression: propensity scores, diff-in-diff, instrumental variables
• Experience with a modern data warehouse (Snowflake, BigQuery, Redshift) and BI tooling (Looker, Tableau)
• Bachelor's degree in a quantitative field (Statistics, Math, Computer Science, Economics) or equivalent practical experience
• Bonus: Master's/PhD in a quantitative discipline; experience with causal ML (uplift modeling, CATE estimation); MLOps exposure (Airflow, MLflow); prior experience owning metrics definitions for a product line