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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Bestow Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $125,000.0 - $140,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Business Analytics Applications, BigQuery, Cloud Database, Code Review, Continuous Delivery, Continuous Integration, Cursor (Graphical User Interface Elements), Monitoring of Systems, Python (Programming Language), Machine Learning, Software Deployment, SQL Databases, Feature Engineering, Sql Optimization, Large Language Models, Multi-Agent Systems, Production Code, Performance Monitor, Software Version Control - **Published:** September 4, 2026 - **Apply:** https://www.builtincolorado.com/job/senior-data-scientist/11011841?handler=ApplyRedirect ## About the Role * 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. COMPETENCIES Machine Learning Model Development - Builds traditional ML models (classification, regression, anomaly detection) from proof of concept through production, with rigorous feature engineering and validation. ## Description 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., As a Senior Data Scientist on the Data & Analytics team, you'll split your time across two areas: building and productionizing machine learning models, and building the agentic AI tools and data products that turn one-off analysis and modeling work into durable, self-serve capability. This role is perfect for someone who is equally happy digging into a funnel conversion question, shipping a production model with monitoring in place, and building the agent that means no one has to ask the question manually again. * 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., 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Are Code Reviews Worth It? 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