SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Bestow is a vertical technology platform modernizing life insurance by unifying legacy infrastructure and enabling carriers to launch products in weeks instead of years. The platform is powered by AI, automation, and data-driven decision-making.
As a Senior Data Scientist on the Data & Analytics team, you'll own two primary areas: building and productionizing machine learning models, and developing agentic AI tools and data products that transform one-off analysis into self-serve capabilities.
Key responsibilities include:
- Developing traditional ML models (classification, regression, anomaly detection) from proof of concept through production deployment
- Owning the complete model lifecycle: feature engineering, validation, deployment, performance monitoring, drift detection, and retraining
- Partnering with engineering to productionize models within Bestow's existing pipelines and platforms
- Building internal analytics products, from dashboards and self-serve reporting to natural-language and agentic interfaces that enable non-technical stakeholders to query data and model outputs
- Building LLM-powered and agentic applications with proper evaluation, monitoring, and human oversight appropriate for a regulated industry
- Driving adoption by documenting, training, and enabling business stakeholders to self-serve on tools and agents
- Automating recurring analytical and modeling work into pipelines and monitoring systems that detect anomalies and surface insights proactively
- Writing production-grade Python and SQL code with version control, code review, testing, and CI/CD practices
- Raising team standards for data quality, documentation, and modeling practices
You bring 5+ years of professional data science experience with a proven track record of shipping ML models and data products to production. You're fluent in SQL and Python, experienced with cloud data warehouses (BigQuery preferred), and have hands-on experience building traditional ML models through production deployment. You have strong statistical fundamentals, hands-on experience with LLMs and agentic systems, and use AI coding agents as part of your daily workflow. You've driven real adoption of tools you've built, communicate clearly with non-technical stakeholders, and own your work from scoping through adoption.
