Principal Data Scientist - Identity and Matching Models

Spokeo•Pasadena, CA

9d•$166,500 - $216,000•Remote

About The Position

Every Spokeo search result depends on one decision: do these records describe the same person, and how sure are we? You will build the models that make that call, on messy data from many vendors and public sources, where there is no answer key to check against. You will own a modeling problem from the first question to the production release, and you will see your work change what customers see. The Data Science team is small, so you work directly with the Director of Data Science and the product owners. If you like designing the test as much as building the model, and you want to mentor teammates as the team grows, this is the place to do it.

Requirements

6+ years of applied data science, though what you have shipped matters more than the count.
A Bachelor’s degree in a quantitative field is expected, and a master’s is preferred, or equivalent professional experience.
Taken a model from idea to production in a cloud setting (AWS SageMaker, Databricks, MLflow, or similar), monitored it, and measured how it performed after launch.
Worked with very large datasets (100 million rows or more) using Spark or a similar tool, with Python and SQL.
Judged whether a model is good when no clean answer key exists, for example by sampling and hand-checking results, and said how confident you are in the number.
Told a real improvement from noise, and explained the result, its limits, and the tradeoffs in plain language to engineers and product owners alike.
Kept a model healthy as its data changed: knowing when labels and models have gone out of date and need refreshing, and writing the process down so a teammate can pick it up.
Handled personal data responsibly, following opt-out, retention, and lineage rules without being reminded.

Nice To Haves

Experience matching or de-duplicating records, using AI to help label data, graph methods, or turning a raw model score into a confidence a product team can act on, is a plus.

Responsibilities

Build and improve the models that match records and score how confident we are in each result. You will choose the approach, train it, test it honestly on held-out data, and ship it.
Design the labels and the measurement. Decide what to hand-check, build the evaluation sets, and estimate how often we are right when a full answer key does not exist. This includes using AI models to help label at scale, and checking them against human judgment.
Take models to production on AWS, with ML engineering and DevOps. That covers the retrain schedule, the model registry, and a record of where every model and its data came from.
Review each data build before it ships. Find what changed, catch regressions, and explain it in plain language to product and sales partners.
Work with the Product Owner to turn findings into stories with clear targets, and defend the priority of the next research spike.
Document your methods, review teammates' code, and take part in the DS/ML working group.