Data Scientist

Evlo AI; Seattle (WA); On-site; USD 120,000 - 180,000; Full time

Job summary

Evlo AI is seeking a hands‑on data scientist who turns messy, large‑scale behavioral and transactional data into predictive models that drive real product and business decisions—not dashboards that nobody reads.

Working within a cross‑functional squad of ML engineers and product owners, the data scientist owns the full analytical lifecycle: framing ambiguous problems, building predictive models, running rigorous experiments, and shipping insights that survive stakeholder scrutiny.

Qualifications

• 3–6 years of data science or ML experience with production deployments.
• Expert-level SQL and strong Python (pandas, NumPy, scikit‑learn).
• Solid grounding in statistics: hypothesis testing, causal inference, regression, time‑series methods.
• Experience designing and analyzing controlled experiments (A/B tests) at scale.
• Track record of communicating technical findings to non‑technical stakeholders through clear writing and presentation.
• MS or PhD in a quantitative field (Statistics, CS, Economics, Physics, Applied Math) or equivalent industry experience.

Responsibilities

• Design, build, and productionize predictive models (forecasting, churn, propensity, ranking) using Python, scikit-learn, and PyTorch.
• Develop and maintain feature pipelines in SQL and PySpark against petabyte‑scale data warehouses (Snowflake, Databricks).
• Design and analyze A/B tests and quasi‑experiments; communicate statistical findings with clear confidence levels and business impact framing
• Partner with ML engineers to deploy models via containerized services (Docker, AWS) and define monitoring for drift and performance degradation
• Build reusable analytical tooling and dashboards in dbt, Airflow, and Tableau that reduce one‑off analysis work across the org
• Translate ambiguous product questions into concrete analytical plans, present results to senior stakeholders, and defend methodology under pressure

Skills

SQL

Python

Statistical methods

A/B testing

Experiment design

Communication

Spark

Docker

LLM workflows

Education

MS/PhD in quantitative field

Tools

dbt

Airflow

Tableau

Snowflake

Databricks

PyTorch

scikit-learn

pandas

Docker

AWS

Job description

About The Role

The role is for a hands‑on data scientist who turns messy, large-scale behavioral and transactional data into models that drive real product and business decisions - not dashboards that nobody reads.

About The Role

The role is for a hands‑on data scientist who turns messy, large-scale behavioral and transactional data into models that drive real product and business decisions - not dashboards that nobody reads. Working within a cross‑functional squad of ML engineers and product owners, the data scientist owns the full analytical lifecycle: framing ambiguous problems, building predictive models, running rigorous experiments, and shipping insights that survive stakeholder scrutiny.

Key Responsibilities

• Design, build, and productionize predictive models (forecasting, churn, propensity, ranking) using Python, scikit-learn, and PyTorch
• Develop and maintain feature pipelines in SQL and PySpark against petabyte‑scale data warehouses (Snowflake, Databricks)
• Design and analyze A/B tests and quasi‑experiments; communicate statistical findings with clear confidence levels and business impact framing
• Partner with ML engineers to deploy models via containerized services (Docker, AWS) and define monitoring for drift and performance degradation
• Build reusable analytical tooling and dashboards in dbt, Airflow, and Tableau that reduce one‑off analysis work across the org
• Translate ambiguous product questions into concrete analytical plans, present results to senior stakeholders, and defend methodology under pressure

What We Are Looking For

• 3–6 years of experience in data science, applied statistics, or ML, with models or analyses that shipped to production
• Expert‑level SQL and strong Python (pandas, NumPy, scikit‑learn); comfort working with large datasets in Spark or similar
• Solid grounding in statistics: hypothesis testing, causal inference, regression, time‑series methods, and their real‑world failure modes
• Experience designing and analyzing controlled experiments (A/B tests) at scale
• Track record of communicating technical findings to non‑technical stakeholders through clear writing and presentation
• MS or PhD in a quantitative field (Statistics, CS, Economics, Physics, Applied Math) or equivalent industry experience
• Bonus: experience with LLM‑assisted workflows, causal inference libraries (DoWhy, EconML), dbt, or prior work in marketplace/subscription businesses