Senior Data Scientist at Berkley
Glen Allen, VA
$150,000–$200,000from the description
Jul 13, 2026
Glen Allen, VA
Jul 21, 2026
What this job asks for AI summary
A senior individual-contributor role focused on building and deploying production-grade machine learning and generative AI systems — including LLM pipelines, RAG architectures, and agentic workflows — across insurance domains such as underwriting, claims, and finance. The position covers the full development lifecycle from prototyping through CI/CD deployment and ongoing monitoring. It suits an experienced applied ML practitioner with strong Python and ML engineering skills who can also shape technical standards and influence strategy.
Mid level · 3+ years · National · Bachelor's required · Full-time
Advertised as Senior, but the requirements read as Mid.
“or” means any one of them counts — you don't need all of them.
Posted 4 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
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). 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 (11)
No work location or metro is specified in the posting; the CBSA is left blank. The role may be remote or hybrid but is not explicitly stated as fully remote — remote is set to false as a conservative default.
The advertised title is 'Senior Data Scientist', but the stated experience gate is only 3–5+ years of hands-on ML/AI experience, which aligns with a Mid-level band. The 'principal-level technical authority' language in the responsibilities section is aspirational framing rather than a structural org-level designation, so seniority is assessed as Mid.
A Bachelor's degree in a quantitative field is listed as a hard requirement under Education; Master's/PhD is noted as preferred. Degree requirement is set to Bachelors accordingly.
Azure ML is listed as the preferred cloud platform; AWS SageMaker and GCP Vertex AI are listed as acceptable alternatives within the same required bullet.
Docker and Kubernetes appear together under containerization in the required Technical Proficiency section; they are listed as alternatives since the JD treats them as a paired/interchangeable containerization requirement rather than both being independently mandatory.
PyTorch and TensorFlow are listed as alternatives ('PyTorch or TensorFlow') in the required section.
LangChain and LlamaIndex are listed as alternatives ('LangChain/LlamaIndex or equivalent') in the required section.
Causal inference, Bayesian modeling, survival analysis, and simulation are all listed under the required Statistics & ML Foundations section. Survival analysis and simulation are omitted as skills since they name methodologies rather than concrete named tools.
The preferred qualifications section covers: financial services/insurance domain experience, open-source ML contributions, real-time inference systems, model governance frameworks, agentic AI/multi-modal/domain-adapted LLMs, and agile/product-oriented delivery — all marked as preferred.
Sponsorship is explicitly not offered for this role.
Ignored 3 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): Bayesian modeling, real-time inference systems, model governance.
Read the full posting
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