Principal Data Scientist, GTM Data Science
SimpliSafe · Boston, MA
$182,000–$242,600from the description
Jul 27, 2026
Boston, MA
Jul 28, 2026
What this job asks for AI summary
A senior individual-contributor data science role focused on go-to-market (GTM) applications — customer acquisition, retention, segmentation, lifetime value, personalization, and next-best-action systems. The position owns technical direction and production ML systems that directly support growth and marketing, and is expected to mentor others and influence roadmaps across teams. It suits an experienced applied ML practitioner with a strong background in marketing science and causal inference.
Principal level · 6+ years · Boston, MA · Full-time
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 5 people in the Boston, MA area plausibly meet what this posting asks for (data scientists). range 3–7
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 $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 (7)
The role is explicitly titled 'Principal Data Scientist' and the JD describes org-wide technical leadership (setting direction, influencing roadmaps, mentoring) — genuine Principal-level scope, not merely a long-tenured Senior.
Location is not explicitly stated in the posting; SimpliSafe is headquartered in Boston, MA, so the CBSA has been inferred from the company's known location.
The advanced degree (CS, Statistics, ML, Economics, etc.) appears only under 'Preferred Qualifications', so the degree requirement is set to None.
Causal inference and incrementality measurement are stated with firm language in the main requirements block and represent a concrete methodological skill gate, so they are marked required.
MLOps is listed as a required capability with firm language ('Significant experience with MLOps, model deployment, model monitoring'); specific tooling beyond MLflow is not named.
Gradient Boosted Trees, neural networks, and transformer-based models appear under 'Preferred Qualifications' and are marked accordingly.
Agentic AI, recommendation/personalization systems, and domain experience (subscription, ecommerce, etc.) are preferred qualifications but name no specific concrete tool, so they are omitted from the skills list per extraction rules.
Read the full posting
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