Staff Data Scientist - MoneyLion
Gen Digital Inc. · New York, NY
$176,000–$191,000
Jul 21, 2026
New York, NY
Jul 23, 2026
What this job asks for AI summary
A senior individual-contributor role on the data science team behind a financial products marketplace, focused on building and owning production machine learning systems end-to-end. Day-to-day work spans feature pipeline construction, model development for recommendations, pricing, and conversion prediction, A/B testing, and hands-on deployment into real-time serving infrastructure. Suits an experienced practitioner comfortable bridging data engineering, ML, and cross-functional collaboration.
Staff level · 7+ years · Remote
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 220 people nationally plausibly meet what this posting asks for (data scientists). range 95–330
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (7)
No work location or CBSA is specified in the posting; the role appears to be remote-eligible based on Gen's stated flexible working options, but no explicit 'remote' designation is given — treated as remote given the absence of any office requirement.
The degree requirement lists Bachelor's or Master's in a quantitative field but explicitly accepts 'equivalent professional experience,' so no minimum degree is hard-gated.
Redshift and Snowflake are named together as the primary data warehouse technologies in the responsibilities section; BigQuery is added as a third option in the requirements section — all three are captured under Redshift with alternatives.
SageMaker, Ray, and MLflow appear together under 'ML platforms and infrastructure' with 'or equivalent,' indicating interchangeability; marked preferred as the section reads as a requirements elaboration but uses softer framing ('experience working with').
Docker, Kubernetes, CI/CD, Scala, and Java appear in a 'Comfortable doing software engineering when needed' clause — the word 'comfortable' and 'when needed' signal these are not hard gates; marked preferred accordingly.
Fintech/financial services/marketplace experience is explicitly called 'a strong plus' — omitted from skills as it is domain context rather than a named technology.
The 'Staff' seniority is supported both by the explicit 'Staff Data Scientist' title and by the scope: end-to-end ownership of critical model systems, leading technical initiatives across multiple product areas, and driving team-wide best practices — genuine cross-team technical leadership.
Read the full posting
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