Senior Data Scientist

We need to output HTML fragment with sections: About the role? Actually the posting includes "About Bestow" (company description) then "About The Team", then "What You'll Do", then "Who You Are", then "COMPETENCIES", then "TOTAL REWARDS Benefits". We need to map headings to allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide which sections correspond. - About the role: could be the description of the role (What You'll Do). Could also be "About The Team". But we need to use headings only when there is real specific content. We have content: "About Bestow" (company overview) - not a role heading; but we could put under "About the role"? Actually "About the role" is for role description. The company overview could be omitted? The instruction: "Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

or drop it." It doesn't forbid including company description; we can put it under a heading like "About the role" or maybe not use a heading and just put in

. But we need to use headings for sections only when we have real specific content. We could have a section "About the role" containing the company overview and team description? Might be mixing. Better: Use headings: About the role (company overview + team), Responsibilities (What You'll Do), Requirements (Who You Are + Competencies), Benefits (TOTAL REWARDS Benefits). Possibly Skills could be separate but we can incorporate into Requirements. We must not create Pay or Schedule sections unless posting states them. No pay/schedule info given, so omit. We need to use

for each section heading. Inside sections, use

• for any list of duties/requirements/benefits, and

  for prose. We need to preserve all substantive information, not invent or summarize away detail. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. Also remove E-Verify mention? That's legal boilerplate; should be removed. Also remove "Thanks for considering a job at Bestow!" maybe boilerplate. We need to keep all substantive content: company description, team description, responsibilities, qualifications, competencies, benefits list. Let's extract. Company description paragraph: "About Bestow Life insurance is one of the world's most important products. It's also one of the hardest to build, distribute, and modernize. Bestow exists to change that. Bestow is a leading vertical technology platform serving some of the largest and most innovative life insurers. Our platform unifies the fragmented, legacy value chain, enabling carriers to launch products in weeks instead of years. Carriers choose us to scale and operate at unprecedented speed, powered by AI and automation. Bestow isn't selling policies. We're building the infrastructure that helps an entire industry move faster, reach more people, and deliver on its promise. Backed by leading investors (Goldman Sachs, Hedosophia, NEA, Valar, 8VC) and trusted by major carriers, Bestow is powered by a team that moves with precision, purpose, and heart. If you want to help reimagine a centuries-old industry with lasting impact, join us. Bestow offers flexible remote/hybrid work, meaningful benefits, equity, and substantial growth opportunities." We can keep that as prose under About the role. Team description: "About The Team The Data & Analytics team is responsible for the data that powers Bestow and the Bestow Platform: the pipelines, models, and analytical products that our business teams, executives, and carrier partners rely on every day. We sit at the center of the company, partnering with product, actuarial, marketing, operations, and engineering to build the machine learning (ML) models and AI-powered, agentic tools that put data and predictions directly in stakeholders' hands. This role is open to remote employees in the U.S. (48 contiguous states only.)" We can include that also under About the role or maybe separate section About the team but not allowed heading. We'll incorporate into About the role. Responsibilities: "What You'll Do" bullet list. Let's extract each duty as list items. - Build ML models. Develop traditional ML models (classification, regression, anomaly detection, and similar) from proof of concept (POC) through production deployment. - Own the model lifecycle. Handle feature engineering, validation, deployment, and ongoing performance monitoring, drift detection, and retraining. - Productionize with engineering. Partner with engineering to productionize models within Bestow's existing pipelines and platforms. - Build agentic data products. Develop internal analytics products, from dashboards and self-serve reporting to natural-language and agentic interfaces that let non-technical stakeholders query company data and model outputs directly. - Build with LLMs, responsibly. Build LLM-powered and agentic applications with proper evaluation, monitoring, and human oversight, appropriate for a regulated industry. - Drive adoption. Document, train, and enable business stakeholders to self-serve on the tools and agents you build. - Automate the recurring work. Turn recurring analytical and modeling work into pipelines and monitoring systems that detect anomalies and surface insights proactively. - Write production-grade code. Use Python and SQL with version control, code review, testing, and continuous integration/continuous delivery (CI/CD). - Raise the bar. Improve data quality, documentation, and modeling standards, and establish patterns for AI-assisted and agentic analytics workflows across the team. We'll put these as

  • . Requirements/Qualifications: "Who You Are" list. - Experienced. 5+ years of professional experience in data science (or equivalent experience), with a track record of shipping ML models and data products to production. - SQL and Python fluent. You have advanced SQL skills and are experienced with cloud data warehouses (BigQuery preferred), and you write clean, maintainable Python. - A model builder. You have hands-on experience building traditional ML models (classification, regression, or similar) and taking them from prototype through production deployment, including ongoing performance monitoring. - Statistically grounded. You have strong statistical fundamentals underpinning your modeling work: feature engineering, validation methodology, and the judgment to know when a model is (and isn't) the right tool. - Agentic and AI-fluent. You have hands-on experience building with LLMs and agentic systems (prompting, retrieval, tool use, evaluation), and you use AI coding agents (e.g., Claude Code, Cursor) as a core part of your daily workflow, directing them through clear context, explicit constraints, and deliberate planning. - A driver of adoption. You've driven real adoption of tools you've built, not just shipped them: getting stakeholders to actually change how they work. - A clear communicator. You communicate well with non-technical stakeholders, translating requirements into models and tools they can actually use. - An owner. You scope your own work, follow through to adoption, and don't wait to be told what the next question is. - Nice to have. Experience building or contributing to agent-based or LLM-powered analytics platforms; experience in insurance, fintech, or another regulated domain; experience building internal web tools or lightweight frontends for analytics products. We can separate required vs nice-to-have but not needed. Competencies list: could be considered Skills section. We'll put under Skills. Competencies bullet list: - Machine Learning Model Development — Builds traditional ML models (classification, regression, anomaly detection) from proof of concept through production, with rigorous feature engineering and validation. - Production ML Ownership — Owns the full model lifecycle: deployment, performance monitoring, drift detection, and retraining, partnering with engineering to productionize within Bestow's platforms. - Statistical Rigor & Judgment — Applies strong statistical fundamentals and critical thinking, forming an independent point of view and questioning results before sharing them broadly with stakeholders; knows when a model is (and isn't) the right tool. - LLM & Agentic Systems Development — Builds LLM-powered and agentic applications (prompting, retrieval, tool use, evaluation) with the monitoring and human oversight a regulated industry requires. - Data Product & Self-Serve Enablement — Builds and drives real adoption of dashboards, natural-language interfaces, and agentic tools that let non-technical stakeholders self-serve. - Production Engineering (Python & SQL) — Writes production-grade Python and SQL with version control, code review, testing, and CI/CD; comfortable in cloud data warehouses like BigQuery. - Stakeholder Communication & Requirements Translation — Translates business questions from non-technical stakeholders into models and tools they can actually use, and builds enough business acumen to spot where data science work can create new opportunities, not just answer what's asked. - Ownership & Follow-Through — Acts as a self-starter: identifies opportunities and pursues them without waiting to be told, scopes their own work, and drives it through to real adoption. - Team & Communication Fit — Works well within Bestow's collaboration norms: written-first, async-friendly communication, offering suggestions and recommendations that influence outcomes, comfort giving and receiving direct feedback, and maintaining a positive, professional attitude even under disagreement. - AI Competency — Directs AI coding agents (e.g., Claude Code, Cursor) We'll put these as list under Skills. Benefits: TOTAL REWARDS Benefits paragraph. Extract benefits list: - Competitive salary and equity based on role - Policies and managers that support work/life balance, like our flexible paid time off and parental leave programs - 100% paid-premium option for medical, dental, and vision insurance - Lifestyle stipend to support your physical, emotional, and financial wellbeing - Flexible work-from-home policy and open to remote - Remote and WFH options, as well as a beautiful, state-of-the-art office in Dallas’ Deep Ellum, for those who prefer an office setting - Employee-led diversity, equity, and inclusion initiatives - Recent Employer Awards Include Best Place for Working Parents 2023 + 2024 + 2025 - Great Place to Work Certified, 2022 + 2023 + 2024 + 2025 - Built In Best Places to Work, 2022 + 2023 + 2025 - Fortune’s Best Workplaces in Texas 2022 + 2023 - Fortune’s Best Workplaces in Financial Services and Insurance 2022 + 2023 + 2024 We can put as
    • . We must not include Pay or Schedule sections. Now produce HTML fragment. Structure:

      About the role

      ... company overview + team description ...

      Responsibilities

      • ... each duty ...

      Requirements

      ... maybe combine Who You Are and Nice to have? Could be a list.

      • ... each requirement ...

      Skills

      • ... each competency ...

      Benefits

      • ... each benefit ...
      Make sure no extra headings. We need to ensure we didn't include any boilerplate like E-Verify, Thanks for considering, etc. Also remove duplicated lines: there may be duplicated sentences like "Bestow offers flexible remote/hybrid work, meaningful benefits, equity, and substantial growth opportunities." Already included in company overview; we keep it once. Now craft the HTML. Let's write the About the role paragraph combining company overview and team description. Paragraph: "Bestow exists to change that. Bestow is a leading vertical technology platform serving some of the largest and most innovative life insurers. Our platform unifies the fragmented, legacy value chain, enabling carriers to launch products in weeks instead of years. Carriers