Jackson Curtis

Jackson Curtis

I’m a product manager specializing in all things data science. I build products that transform complex statistics and machine learning into useful products that people use and trust.

Experience

Recast

Recast is a marketing analytics startup that builds media mix models to help companies plan and optimize marketing budgets. I was the fourth full-time employee and first non-founder data scientist. In 2024, I transitioned to the Head of Product. During my tenure, we’ve bootstrapped ourselves to over 60 employees and millions in revenue.

May 2024 – Present

Head of Product

  • Launched Recast’s second product line, GeoLift by Recast, an easy-to-use tool for designing and analyzing geographic incrementality tests
  • Designed our Plans feature, enabling marketers to build marketing plans, track performance, get forecasts, and run counterfactual scenarios within the Recast platform
  • Launched our Operator program, allowing data scientists (clients and consultants) to use Recast software to build and deliver MMMs to their own clients. Served as the primary liaison between Operators and Recast
  • Piloted our first customer facing API and MCP to enable clients’ agents to interact directly with the Recast tools and data
  • Managed a junior PM and a design contractor and collaborated with the team of ~15 engineers and researchers to build products

Feb. 2022 – May 2024

Senior Data Scientist

  • Onboarded clients by collecting & cleaning data, building and validating models, and delivering results
  • Managed ongoing client relationships by educating users, running analyses, and maintaining models
  • Helped organize Recast’s model configuration infrastructure to take spreadsheet-shaped, data scientist-provided configuration data, pull it into R to calculate Bayesian priors, and feed the priors and data into the Stan model-fitting algorithm

Podium

Jan. 2021 – Feb. 2022

Senior Data Scientist

  • Developed a mapping application (in Shiny) of Podium’s current customers to enable field sales blitz planning and allow SDRs to find useful reference customers
  • Built time series models to forecast monthly sales, gross processing volume, and customer counts to assist goal setting and track progress towards KPIs

Qualtrics

May 2019 – Jan. 2021

Advanced Research Analyst

  • Performed quantitative market research consulting for Qualtrics’ clients using methodologies such as Conjoint/MaxDiff (hierarchical Bayesian multinomial models), Gabor Granger, TURF, and regression with an average billing of $100k/quarter for my work
  • Developed interactive applications to share results with clients (replacing Excel tools)

Eli Lilly and Company

May 2018 – Aug. 2018

Business Analytics Intern

  • Built a web app (Shiny) that implemented a way to model, forecast, and visualize risk associated with a portfolio of insurance contracts

Intermountain Healthcare

May 2017 – Sept. 2017

Population Health Analytics Intern

  • Developed predictive models (Random Forest and XGBoost) to identify high-cost candidates for care management programs, focused on curating cost data to ensure the list was actionable for care managers

Education

Brigham Young University

April 2019

MS/BS in Statistics; 3.97 GPA

Minors in Computer Science and Mathematics

  • Masters project on estimating system reliability using a nonparametric Bayesian model.

Skills

Product

  • Product strategy for ML-driven products
  • Communicating model output to non-expert users
  • API & MCP design
  • 0→1 product launches
  • Product marketing

Data Science

  • Applied Bayesian modeling
  • Experimentation & causal inference
  • Incrementality & lift measurement
  • Forecasting & optimization
  • Model evaluation

Engineering