Bristol Myers Squibb · Princeton, NJ

Salary
$152,150–$184,370from the description
Posted
Jul 7, 2026
Location
Princeton, NJ
Last confirmed open
Jul 21, 2026

What this job asks for AI summary

A hands-on technical leadership role focused on building and deploying AI-powered commercial analytics platforms for a pharmaceutical brand portfolio. Core work involves developing Bayesian marketing mix models, agentic AI workflows, RAG pipelines, and production-grade dashboards and applications, while also managing data engineering, responsible AI governance, and stakeholder enablement. Best suited to a quantitative professional with experience in pharmaceutical commercial analytics, Python-based data science, and cloud platforms.

Senior level · 3+ years · New York-Newark-Jersey City, NY-NJ-PA · Master's required · Full-time

Must have (18)
PythonPandasPyMCScikit-learnPolarsDatabricks or AWS RedshiftRAGLLMsAzure OpenAIStreamlit or DashReactGitDockerCI/CDCeleryRediscausal inferenceNLP

“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 95 people in the New York-Newark-Jersey City, NY-NJ-PA area plausibly meet what this posting asks for (data scientists). range 20–140

Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.

What gives you an edge
Celery5%RAG8%

Rare in this occupation — lead with these, and say what you built with them.

What won't set you apart
Python88%Pandas72%Scikit-learn58%Docker53%CI/CD45%React40%

Most people in this occupation already list these. Still required — just not what gets you shortlisted.

What the occupation pays Median $138,970 (middle half $103,548–$176,968). 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 (10)

The posting is titled 'Sr. Manager' but the role is explicitly described as a 'hands-on technical leader and platform engineer' with no direct-report or people-management responsibilities mentioned; seniority is assessed as Senior IC rather than a management band.

A Master's degree in a quantitative field is explicitly required; a PhD is stated as preferred, not required.

The overall experience gate is 3+ years in pharmaceutical commercial analytics specifically; the 1-year Marketing Mix Modeling requirement is a separate, named-skill gate.

Databricks and AWS Redshift are listed together as cloud data platform examples; they are treated as alternatives for that capability gate. AWS Redshift is also listed separately as a preferred/secondary option given the phrasing 'experience with cloud data platforms (Databricks, AWS Redshift)'.

Streamlit and Plotly Dash are listed as interchangeable dashboard/app development tools in the same parenthetical; React.js is listed alongside them but serves a distinct purpose (chatbot/scenario simulation interfaces) and is emitted separately.

Celery and Redis appear under 'working knowledge of distributed computing, task orchestration' — they are listed as named tools in the required qualifications section and are treated as hard gates.

Causal inference methods (geo-experiments, matched markets, synthetic controls, uplift modeling) are listed in the required qualifications; 'causal inference' is emitted as the canonical skill name covering these techniques.

The role is located in Princeton, NJ, which falls within the New York-Newark-Jersey City CBSA. The posting indicates a hybrid/site-by-design occupancy model (at least 50% onsite), so remote is set to false.

Additional incentive cash and stock opportunities are mentioned but no specific figures are given; only the base salary range is captured.

Ignored 3 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): Marketing Mix Modeling, Bayesian modeling, multi-agent orchestration.

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