Data Scientist

About the role

ABOUT REPLAY

We are building the first-ever bank for enterprise data. There are over 35 million businesses in the US alone, many of which are sitting on an asset they don’t know they have. At Replay, we help businesses monetize their historical operating data by licensing it to the frontier AI and data labs for model training. Replay sits at the intersection of advancing the frontier of AI, while helping companies generate millions in growth capital without having to give up equity, take on debt, or lay off employees.

WHY JOIN REPLAY NOW

- We have scaled from $0 to a multi-eight-figure run rate in a matter of months

- We have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle Fund

- We are small enough that you will carry outsized responsibility and grow as quickly as the company does

- You will partner with and build for some of the fastest and most important companies in the world

- You will help build a massive, category-defining business from the ground floor

THE ROLE

Replay turns sensitive internal enterprise data into de-identified datasets without destroying the structure and meaning that make the data valuable. That creates a difficult measurement problem. A system can improve aggregate F1 while missing a high-risk slice, remove more sensitive information while also destroying useful context, or pass one stage while defects escape somewhere else in the pipeline.

As Replay's first Data Scientist focused on evaluation, you will establish how we know whether that data is actually getting better. You will build the datasets, experiments, quality measures, and feedback loops that expose hidden failures, accelerate model and pipeline improvement, and give the team confidence in what it delivers.

This is a hands-on, zero-to-one role at the intersection of data science, AI, and a real production system. You will write Python and SQL, construct evaluation corpora, study failure patterns, design comparisons, calibrate human and model-based judgments, and turn the result into a clear decision. The questions are scientifically difficult, but the output must be practical enough to change what the team builds and ships.

You will work closely with Machine Learning, Product Engineering, Data Engineering, Security, Quality, domain experts, and the team making delivery decisions. Machine Learning Engineers own changing model behavior. You own the credibility of the evidence used to decide whether a model, pipeline, or delivery change actually made the data safer or more useful.

QUESTIONS YOU MIGHT ANSWER

- Did a higher NER or entity-resolution score actually reduce sensitive misses across the messages, documents, tables, and providers that matter?

- Is a new model finding more sensitive information, or simply removing more of the useful structure our customers need?

- Can we trust a golden dataset, a human review process, or an LLM judge enough to use it for a release decision?