Head of Engineering & Data Science

Theta is a predictive customer value analytics firm. The Head of Engineering & Data Science will own engineering and lead the data science team, scaling the company’s modeling and automation systems while setting technical direction and working with clients’ technical teams.

Responsibilities:

• Architecting reliable execution of thousands of models on our cloud and working with client engineering teams to package and deploy our models in their own environments
• Making our pipeline from raw client files to validated model outputs reliable and reusable, while developing AI-assisted workflows for data preparation and modeling
• Setting standards for testing, validation, and reliable deployment, and reviewing the team’s code and designs
• Deciding what we build, automate, or bring in outside help for, while ensuring the team can maintain what gets built
• Helping data scientists resolve difficult client delivery problems and contributing directly when engagements overlap

Requirements:

• You've built and operated production data or ML systems end to end, at scale on cloud infrastructure (GCP preferred, AWS, Azure, Databricks, or Snowflake is also relevant)
• You've solved problems across data preparation, model execution, and infrastructure, from messy input files to slow model runs
• You check data and outputs critically and know which issues should stop a run
• You're willing to learn and work in our existing R-based production stack
• You think like a product engineer, building around how data scientists and clients actually work and balancing reliability, usability, and delivery needs
• You can lead a small, experienced data science team through hands-on technical guidance and resolving difficult delivery problems
• You're comfortable participating in client calls to explain technical methods and trade-offs, discuss data requirements, and understand clients' technology stacks and infrastructure
• You have a genuine interest in developing AI-assisted workflows, learn unfamiliar tools quickly, and critically evaluate their outputs
• Experience building and evaluating AI-assisted workflows for data preparation and modeling, including LLM or agent-based systems
• Production R experience, including packaging, dependency management, containers, or C++ integration through Rcpp
• Statistical modeling, especially familiarity with CLV or BTYD models
• Experience deploying data or modeling workflows in client-controlled environments, including work with Databricks, Snowflake, or Spark
• Infrastructure as code experience, such as Terraform or similar, and CI/CD