Staff Data Scientist, LTV

This is a fully remote position, open to applicants in United States.

📋 Description

• Act as a senior technical authority for the Lifetime Value team while staying actively involved in exploratory analysis, model development, deployment, monitoring, and production support.

• Oversee intricate initiatives across interconnected models that forecast customer conversion, retention, future premium, and claim losses.

• Define ambiguous modeling challenges, assess analytical methods, and refine technical strategy.

• Examine interactions among component models, troubleshoot underperformance, and prioritize improvements based on business impact.

• Design and validate experiments and measurement frameworks with well-defined success criteria.

• Evaluate model performance and business implications post-launch.

• Collaborate with the team manager on quarterly planning, sequencing, capacity, milestones, and interdependencies.

• Partner with machine learning engineers and technology teams to facilitate the production deployment of models, simulations, and forecasting workflows.

• Maintain a balance between rigor, reliability, interpretability, and speed of delivery.

• Articulate recommendations, risks, and trade-offs to technical partners, business leaders, and senior executives.

• Mentor and guide other data scientists.

• Create reusable methodologies, tools, and standards that enhance data science efforts across the Lifetime Value team and related Quantitative Science initiatives.

⛳️ Requirements

• BS, MS, or PhD in Statistics, Computer Science, Economics, or a related quantitative discipline.

• 8+ years of experience in delivering complex, impactful data science projects, including predictive modeling, experimentation, and business decision support.

• Strong expertise in survival analysis, including time-to-event modeling and censoring.

• Proficient in statistical modeling, forecasting, experimental design, and validation.

• Software engineering proficiency in Python, including modular, tested, well-typed, and readable code.

• Experience in maintaining and refactoring a large shared codebase.

• Experience in constructing and managing systems of interacting models, such as ensembles or chained predictions.

• In-depth knowledge of Python and SQL.

• Extensive hands-on experience with contemporary modeling and experimentation frameworks.

• Strong grasp of statistical methods, predictive modeling algorithms, survival analysis, time-series forecasting, experimental design, measurement, and validation.

• Experience in developing and maintaining interconnected production models using MLOps practices, including feature stores, training and inference pipelines, workflow orchestration, version control, and post-deployment monitoring.

• Ability to assess the potential value of modeling initiatives and analyze model performance and business effects after deployment.

• Excellent communication and relationship-building abilities.

• Proven track record of influencing priorities and technical direction across related workstreams while ensuring accountability for hands-on delivery.

• Capability to guide technical endeavors, mentor data scientists, and establish reusable modeling, experimentation, validation, or reporting methodologies.

• Familiarity with customer lifetime value forecasting, simulation workflows, forecast-versus-actual analysis, or causal inference.

• Experience in insurance or regulated financial products.

• Proficiency with cloud-based data and machine learning platforms and tools such as AWS, Docker, dbt, Airflow, Metaflow, Step Functions, or MLflow.

• Experience in creating visualizations, dashboards, or reporting.

• Experience in prototyping new modeling techniques or data science tools.

• Must appear on camera for virtual interviews.

🏝️ Benefits

• Competitive bonus structure.

• Equity offering available.

• Flexibility to work from any location within the US.

• Reasonable accommodations provided throughout the hiring process.