Data Scientist, Baseball Analytics at Boston Red Sox
Boston, MA
$75,000–$100,000from the description
Jun 30, 2026
Boston, MA
Jul 21, 2026
What this job asks for AI summary
A data science role embedded in the Baseball Analytics group, focused on building and maintaining predictive models and data pipelines to support player evaluation, acquisition, and development. Day-to-day work spans applied research, machine learning, data visualization, and translating findings for non-technical stakeholders. Suits candidates with a quantitative graduate degree or equivalent experience and a working knowledge of baseball.
Mid level · Boston-Cambridge-Newton, MA-NH · Full-time
“or” means any one of them counts — you don't need all of them.
Posted 5 times — it's one opening, so apply once.
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 1,650 people in the Boston-Cambridge-Newton, MA-NH area plausibly meet what this posting asks for (data scientists). range 1,250–2,150
Applicant volume Heavy — This req sits in a large pool with little in its requirements to thin it, and auto-apply tools fire at everything in the occupation. Applying early and leading with the rare skills below is what gets read.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $134,944 (middle half $104,979–$169,773). This posting is about at that midpoint.
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 (5)
The posting explicitly invites applicants 'with varying levels of expertise' and states the role, level, and compensation will be tailored to the selected candidate — seniority is assessed as Mid as the default for an unleveled, independently-delivering data science role, though the band could reasonably span Junior through Senior.
Degree requirement is marked None: the JD lists a PhD or master's as a qualification but explicitly accepts 'equivalent professional experience' as an alternative, so it is not a hard gate.
Python and R are listed together as alternatives ('R or Python'); Python is named first here as the more common primary, with R in alternatives.
Machine learning and statistical modeling are named as core competencies in the Competencies section rather than a formal Requirements block, but the language ('advanced proficiency', 'understanding of modern statistical and machine learning methods') is firm enough to treat them as hard gates.
No specific ML frameworks, visualization libraries, or data pipeline tools are named — the JD refers only to 'popular data science languages and libraries' generically, so no additional tool-level skills can be extracted without invention.
Read the full posting
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