Our data team is organized across three groups: Data Engineering, Data Science, and Strategic Analytics. Data Science owns the modeling work that drives Perpay’s most consequential decisions: credit decisioning, loss forecasting, marketing-mix attribution, product experimentation, and the ML systems that sit in front of our customers in real time. This year, with the credit portfolio scaling and our modeling needs getting heavier, focus areas include owning the data science side of the risk decisioning service redesign, expanding our card-portfolio modeling, deepening our use of LLMs in both internal workflows and customer-facing surfaces, and tightening the feedback loops between our credit-reporting strategy and the data that informs it. Data Science partners directly with Engineering, Risk, Marketing, Merchandising, and Finance, and works hand-in-hand with Data Engineering and Strategic Analytics on shared infrastructure and shared problems.
Our data science culture leans toward end-to-end ownership: the person who designs a model should be the one who scopes it with stakeholders, ships it to production, and stays close to how it performs once it is live. We invest in rigor where rigor matters and resist the urge to over-engineer where it does not. We are comfortable being challenged on our work and comfortable challenging back, because the alternative is shipping models that look right and are not. The stack: Python everywhere, with the standard data science toolset (scikit-learn, pandas, NumPy, matplotlib, statsmodels) and Bayesian tooling (PyMC) on the projects that need it. Models are deployed and orchestrated on AWS using ECS, Airflow, and Terraform, with Redshift as the underlying warehouse. We use modern LLM tooling where it materially improves the work or the throughput of the team. This role is roughly half individual contribution and half management. You should expect to be writing code, building models, and shipping production work alongside the team, not just reviewing it or unblocking others. You should have at least three years directly managing data scientists, on top of substantial IC experience that you have kept current. If you have grown out of wanting to be in the work, this is the wrong role.
What to Expect from the Role
You will report directly to the Head of Data and lead a Data Science team that spans early-career ICs through senior ICs. The role owns hiring, performance management, and technical strategy for the function, and partners closely with the Head of Data and the leads of Data Engineering and Strategic Analytics on broader org direction.