Theta
Theta
Theta develops predictive customer analytics and corporate valuation methods for investment firms and corporations. Its CLV Ultra model helps teams forecast customer behavior and assess customer unit economics, supporting marketing decisions, customer engagement strategies, and business valuation.

Head of Engineering and Data Science (Remote, United States)

Lead engineering and data science at Theta as it scales a predictive customer value analytics platform. Guide cloud model execution, AI-assisted workflows, and delivery across both disciplines.

Aprašymas

  • Lead Theta’s engineering function and data science team
  • Set technical direction and help resolve complex delivery challenges
  • Collaborate with clients’ technical teams
  • Design reliable cloud infrastructure for running thousands of models
  • Partner with client engineering teams to package and deploy models in their environments
  • Build a reliable, reusable pipeline from raw client files to validated model results
  • Create AI-assisted workflows for data preparation and modeling
  • Establish standards for testing, validation, and dependable deployment
  • Review the team’s code and technical designs
  • Choose what to build, automate, or outsource while keeping systems maintainable
  • Help data scientists solve challenging client delivery problems and contribute directly when engagements overlap

Reikalavimai

  • Strong engineering judgment across software engineering, data science, and infrastructure
  • Experience building and operating production data or machine learning systems end to end at scale on cloud infrastructure
  • Experience with Google Cloud Platform preferred; AWS, Azure, Databricks, or Snowflake experience is also relevant
  • Ability to address challenges across data preparation, model execution, and infrastructure, from inconsistent input files to slow model runs
  • Ability to scrutinize data and results and identify issues that should halt a run
  • Willingness to learn and work with an existing production stack built in R
  • Product engineering approach that balances reliability, usability, and delivery needs
  • Ability to guide a small, experienced data science team with hands-on technical leadership and resolve difficult delivery issues
  • Comfort participating in client calls to explain technical methods and trade-offs, discuss data needs, and understand clients’ technology stacks and infrastructure
  • Interest in building AI-assisted workflows, ability to learn unfamiliar tools quickly, and judgment to evaluate their outputs critically
  • Preferred: Experience building and assessing AI-assisted workflows for data preparation and modeling, including large language model or agent-based systems
  • Preferred: Production R experience, including packaging, dependency management, containers, or C++ integration through Rcpp
  • Preferred: Statistical modeling experience, especially familiarity with CLV or BTYD models
  • Preferred: Experience deploying data or modeling workflows in client-controlled environments, including Databricks, Snowflake, or Spark
  • Preferred: Infrastructure-as-code experience, such as Terraform or similar tools, and CI/CD experience

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