GTM Data Scientist at Bevi
Boston, MA
$120,700–$149,100
Jul 27, 2026
Boston, MA
Jul 28, 2026
What this job asks for AI summary
A data scientist role at Bevi focused on go-to-market analytics spanning both customer health and marketing effectiveness. The work centers on building churn and propensity models, look-alike prospect models, marketing mix models, and incrementality analyses to guide Sales, Marketing, and RevOps decisions. Best suited to someone with hands-on predictive modeling and causal inference experience who can operate independently and communicate findings to non-technical stakeholders.
Mid level · 2+ years
“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
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)
The job title is 'GTM Data Scientist' with no seniority level in the title; advertised seniority is Unspecified. The 2–4 year experience range and scope of independent modeling work support a Mid-level classification.
Location is not explicitly stated in the posting; the #LI-HYBRID tag indicates a hybrid arrangement (not fully remote), but no city or metro is named.
Python and R are listed as interchangeable alternatives ('Python/R'); Python is captured as the primary with R as an alternative. Both are hard-gated by the requirements section.
Looker, PowerBI, and Hex are listed as examples under data visualization experience in the requirements section, but the phrasing 'e.g.' signals any one of them satisfies the requirement — all three are captured as preferred/interchangeable rather than individually hard-gated.
Logistic regression, gradient boosting, causal inference, incrementality testing, difference-in-differences, and MMM are all explicitly named in the requirements block and treated as hard gates on the underlying capability, even though 'or similar' qualifiers appear alongside some of them.
No compensation range is disclosed in the posting.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): marketing mix modeling.
Read the full posting
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