Theta has been revolutionizing the world of private equity, corporate finance, and strategic management through Customer-Based Corporate Valuation (CBCV), an emerging, award-winning methodology for measuring and managing corporate valuation through quantitative models for customer behavior.

​We are looking to hire a Data Scientist. If you are excited about what we do, if you are a smart and motivated learner with solid fundamental data science skills, who is eager to become an expert in probability models, customer lifetime value (CLV), and CBCV, join our team! You’ll have the rare opportunity to learn and apply these growing and increasingly important topics alongside the world-renowned experts in CLV and the authors of CBCV – our founders and Professors Peter Fader and Daniel McCarthy.

We’re an ultra-fun place to work (despite any preconceived notions you may have about “corporate valuation”)! We’re remote-first (since inception), we enjoy flexible schedules, we emphasize work-life balance, and we value connection and collaboration across our distributed teams.

Key Responsibilities:

  • Maintain a deep understanding of probability models for customer behavior; coach junior data scientists on the mathematical underpinnings of our modeling approaches
  • Independently lead and execute CBCV analyses for our private equity and corporate clients, owning all aspects of a project (from statistical modeling to client communication)
  • Independently lead and execute CBCV analyses of pre-IPO and public companies based on publicly available data
  • Design and implement new features for our internal toolkit for statistical modeling
  • Contribute to our methodology research and development efforts (e.g. specify, implement, and validate new modeling approaches)
  • Work with Software Engineers to bring our statistical processes to life at scale
  • Wear multiple hats including business strategy, product development, and software engineering

Basic qualifications (you don’t need all of these to apply!):

  • Bachelor's, master’s, or doctorate in a technical field (math, statistics, economics, quantitative finance, or computer science)
  • Academic or industry research experience in a technical field
  • Strong understanding of probability fundamentals
  • Experience building and owning machine learning or other statistical models
  • Proficiency in multiple programming languages, including R
  • Working knowledge of version control systems, e.g. Git, to contribute effectively within a shared codebase
  • Strong technical communication skills (verbal and written)

Preferred qualifications:

  • Solid understanding of CLV models in contractual and non-contractual settings (e.g., BTYD) is a plus, but not required
  • Professor Fader’s MKTG 476/776 or Professor McCarthy’s MKTG 462/562 class is a plus, but not required
  • Experience with time-series analysis
  • Experience with C++ or Python (specifically Django)
  • Working knowledge of Unix/Linux

If you want to take a peek at what probability models and our work are about, here are a few introductory and more advanced resources:

Introduction to probability models

Foundations of Customer-Based Corporate Valuation (advanced)

Practical applications of Customer-Based Corporate Valuation