Engineering Manager - Experimentation Data Infrastructure

United States Digital Space LLC; San Francisco (CA); On-site; USD 210,000 - 320,000; Full time

Benefits offered by this job

Health insurance

401(k)

Flexible time off

Wellness stipends

Parental benefits

Mental health support

ESPP

Job summary

Statsig seeks an Engineering Manager to lead the Data Infrastructure team within Statsig Experiment, guiding software engineers, data engineers, and data scientists responsible for scalable experimentation systems.

You will shape technical and scientific direction, translating new statistical methods into production capabilities and partnering with customers to advance experimentation practices at scale.

Qualifications

• Strong data science background with hands-on experimentation or causal inference.
• Experience leading teams that build statistically rigorous, data-intensive products.
• Familiar with variance reduction, sequential testing, Bayesian inference, causal effects modeling.

Responsibilities

• Lead and grow the Data Ingestion, Experiment Computation, and Stats Engine teams.
• Define technical and scientific strategy for experimentation across cloud and warehouse deployments.
• Partner with data scientists and engineers to translate methods into scalable product capabilities.
• Evolve data and computation architecture for complex experiments and customer datasets.

Skills

Data science background

Experimentation

Causal inference

Leading teams (10-15)

Distributed computation

Data ingestion

Statistical methods

Education

Advanced degree in statistics

Statistics / mathematics / CS

Tools

Snowflake

BigQuery

Databricks

Job description

Statsig seeks an Engineering Manager to lead the Data Infrastructure team within Statsig Experiment, guiding software engineers, data engineers, and data scientists responsible for scalable experimentation systems.

You will shape technical and scientific direction, translating new statistical methods into production capabilities and partnering with customers to advance experimentation practices at scale.