Product Data Scientist at Bevi
Boston, MA
$120,700–$149,100
Aug 3, 2026
Boston, MA
Sep 24, 2026
What this job asks for AI summary
A product-focused data science role at an IoT beverage company, responsible for owning analytics across digital product experiences and web properties, measuring the incremental impact of new flavor launches using A/B and quasi-experimental methods, and building a machine-level flavor recommendation engine. The role suits a self-directed analyst-scientist hybrid comfortable with both web analytics instrumentation (GA4/GTM) and predictive modeling in SQL and Python or R.
Mid level · 2+ years · Boston-Cambridge-Newton, MA-NH · Full-time
Pay in the description: $120,700–$149,100
“or” means any one of them counts — you don't need all of them.
Posted 3 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 1,200 people in the Boston-Cambridge-Newton, MA-NH area plausibly meet what this posting asks for (data scientists). range 360–1,850
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 Aug 5, 2026. It is a model, not a headcount.
Why we read it this way (6)
The role sits at the intersection of product analytics and data science — it requires both web analytics ownership (GA4/GTM tracking implementation) and ML/modeling work (recommendation engine, causal inference). SOC 15-2051 (Data Scientists) was chosen over 15-2031 (Operations Research Analysts) because the modeling and ML components are prominent, but the split is genuine.
Python and R are listed as interchangeable alternatives ('Python/R'); Python is named first and is the more common choice in this context.
Quasi-experimental methods (difference-in-differences, propensity score matching, pre/post analysis) are explicitly required alongside A/B testing — captured under the A/B testing skill as the posting treats them as a unified experimentation requirement.
Data visualization tools (Looker, PowerBI, Hex) are listed with 'e.g.' framing in the requirements section; they are treated as a required capability with the specific tools being interchangeable options.
The posting is tagged #LI-HYBRID, indicating a hybrid work arrangement rather than fully remote. The company appears to be based in Boston, MA based on the Mass AI Coalition reference and general company context.
AI tools usage is listed in the requirements section but framed as a workflow/mindset expectation rather than a specific named technology gate — marked as preferred accordingly.
Read the full posting
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