Join Our Team

Data Science Contractor

Remote

About the Role

We're looking for an experienced Data Scientist to join us on a contract basis to enhance our existing machine-learning models and develop a new predictive Lifetime Value (pLTV) model using Bayesian methods (PyMC, BTYD framework).

You'll work closely with our data engineering and analytics teams to refine model performance, validate results, and deliver actionable insights that drive decision-making.

This is a hands-on project role ideal for someone who enjoys improving production-grade models and building probabilistic models from real data.

What You'll Do

Review and improve existing predictive models (classification & regression) for donor behavior and revenue forecasting.
Design and implement a pLTV model using Bayesian techniques (e.g., Pareto/NBD, Gamma-Gamma models) in PyMC.
Conduct feature engineering and data exploration to identify variables that improve model performance.
Validate and compare model results using relevant metrics (e.g., MAE, MAPE, calibration plots).
Communicate findings clearly through documentation, notebooks, and brief presentations to technical and non-technical stakeholders.
Collaborate with our engineering team to ensure models are structured for deployment and reproducibility.

What We're Looking For

Strong hands-on experience in Python (pandas, scikit-learn, PyMC, or similar Bayesian libraries).
Proven ability to build and evaluate machine learning models (classification, regression, time-series, or survival analysis).
Familiarity with Bayesian inference and probabilistic modeling (preferably pLTV / BTYD frameworks).
Strong understanding of model validation, overfitting, and uncertainty quantification.
Ability to explain technical results to non-technical audiences.
Bonus: experience with data from donations, subscriptions, or e-commerce environments.

Engagement Details