Head of Engineering and Applied AI

Location: US, Remote

About Theta

Theta is a predictive customer value analytics firm that helps companies and private equity investors understand what their customers are worth and use that insight to make better growth and investment decisions. Co-founded by Peter Fader and Daniel McCarthy, world-renowned experts in customer valuation, we’ve applied our models across hundreds of companies spanning a wide range of industries. Our core offerings include predictive customer lifetime value (CLV) and Customer-Based Corporate Valuation, both powered by our proprietary model, CLV Ultra.

Today we’re a small, experienced team delivering this work through client engagements. We’ve built internal tooling for data ingestion, profiling, pipeline orchestration, and compute, alongside early AI agent-based systems that support data preparation and modeling. These systems give us a working foundation to build on.

The next stage is scale: running thousands of models per engagement, working with client engineering teams to deploy in their own environments, and developing reliable AI-assisted workflows that reduce manual work. The opportunity is to shape how Theta delivers its models at scale while keeping the systems practical for the data scientists and clients who use them.

The role

You’ll lead that next stage, owning Theta’s engineering and leading our data science team, with most of your time spent building. You’ll set technical direction, help the team resolve difficult delivery problems, and work with clients’ technical teams.

Our modeling and automation stack runs in R, with performance-critical modeling code in C++. Your job is to extend and scale that foundation.

What you'll work on

• 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.

Minimum qualifications

We’re looking for someone with deep engineering skills and the curiosity and judgment to work across the rest.

• 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.

Preferred qualifications

• 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.