Job Description
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Company Overview
The employer is a leading AI analytics and experimentation technology company that helps organizations build better digital products and experiences. Its platform supports thousands of customers and enables product, data, and marketing teams to analyze, test, and optimize user experiences through advanced analytics, experimentation, and AI-powered capabilities.
What You’ll Do
- Lead and grow the team responsible for data ingestion, experiment computation, and the statistics engine.
- Define the technical and scientific strategy for advancing experimentation across cloud and warehouse-native deployments.
- Partner with data scientists and engineers to turn new statistical and causal inference methods into scalable and reliable product capabilities.
- Evolve data and computation architecture to support increasingly complex experiment designs, metrics, and customer datasets.
- Engage with customers to understand experimentation challenges and translate those needs into platform and methodology improvements.
You’ll Be a Great Addition to the Team If You Have
- A strong data science background with hands-on experience in experimentation, statistics, or causal inference. Experience solely in data engineering is not sufficient for this role.
- Experience leading teams of 10–15 team members that build and productionize statistically rigorous, data-intensive products.
- Familiarity with experimentation methods such as variance reduction, sequential testing, Bayesian inference, causal effects modeling, or heterogeneous treatment effects.
- Experience building large-scale data ingestion and distributed computation systems across cloud and data warehouse environments.
- The ability to connect statistical innovation, data architecture, and customer needs to define a compelling experimentation roadmap.
Particularly Valuable Experience
- An advanced degree in statistics, mathematics, computer science, economics, or another quantitative field.
- Experience with experimentation platforms, feature management systems, product analytics, or machine learning infrastructure.
- Experience building warehouse-native products or executing computation within Snowflake, BigQuery, Databricks, or similar environments.
- Experience supporting experimentation for large-scale consumer products, B2B products, marketplaces, social networks, or other settings with complex units of analysis.
Why This Role Matters
Experimentation platforms require both strong infrastructure and rigorous statistical research. New methodologies create value when they can be implemented correctly, computed efficiently, and delivered through systems customers can trust.
This Engineering Manager will bring these disciplines together by helping build the data and computation foundation for experimentation while creating an environment where new data science innovations can move from research and prototypes into production.
The role provides an opportunity to contribute to scalable experimentation infrastructure while supporting the development of new approaches to data science and experimentation.
Our Values
The organization emphasizes the following values:
- Humility: Operating with empathy and openness while seeking to understand different perspectives.
- Ownership: Taking initiative to solve problems that contribute to shared success.
- Growth Mindset: Approaching challenges with persistence and using feedback to support personal and team development.
- Customer Centricity: Keeping customers at the center of decision-making and remaining committed to their success.
Disclaimer
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