Root Inc.
Root Inc.
1,001 – 5,000 कर्मचारी
ऑटोमोटिवपरामर्शबीमा
Root Inc. एक उपभोक्ता ऑटो बीमा कंपनी है, जो ड्राइविंग व्यवहार का आकलन करने और पॉलिसियों की कीमत तय करने के लिए मोबाइल ऐप और स्मार्टफोन-आधारित टेलीमैटिक्स का उपयोग करती है। आम तौर पर सुरक्षित ड्राइविंग से दरों पर अनुकूल प्रभाव पड़ता है। इसका ऐप कोटेशन प्राप्त करने, पॉलिसी प्रबंधित करने, दावे दर्ज करने और ड्राइविंग डेटा एकत्र करने के लिए टेस्ट-ड्राइव अवधि की सुविधा देता है। Root कई अमेरिकी राज्यों में सड़क किनारे सहायता और अन्य कवरेज विकल्प भी प्रदान करती है तथा उपयोग-आधारित बीमा में रुचि रखने वाले ड्राइवरों की सेवा करती है।

Staff Data Scientist, Customer Lifetime Value (Root, US Remote)

Lead customer lifetime value modeling at Root, developing and deploying interconnected predictive models for conversion, retention, premiums, and claims. The role combines experimentation, production machine learning, technical leadership, and coaching.

विवरण

  • Provide senior technical leadership for the Lifetime Value team while contributing directly across analysis, modeling, deployment, monitoring, and production support
  • Lead complex initiatives involving interconnected models for customer conversion, retention, future premiums, and claim losses
  • Define ambiguous modeling problems, assess analytical options, and shape technical direction
  • Evaluate interactions between component models, investigate weak performance, and prioritize improvements by business value
  • Create and validate experiments and measurement frameworks with explicit success criteria
  • Evaluate model performance and business outcomes after deployment
  • Coordinate with the team manager on quarterly planning, sequencing, capacity, milestones, and dependencies
  • Partner with machine learning engineers and technology teams to deploy models, simulations, and forecasting workflows to production
  • Balance analytical rigor, reliability, interpretability, and delivery speed
  • Present recommendations, risks, and tradeoffs to technical partners, business leaders, and senior decision-makers
  • Mentor and support other data scientists
  • Build reusable methods, tools, and standards that strengthen data science across the Lifetime Value team and related Quantitative Science work

आवश्यकताएँ

  • Bachelor’s, master’s, or doctoral degree in statistics, computer science, economics, or a related quantitative discipline
  • At least 8 years delivering complex, high-impact data science projects involving predictive modeling, experimentation, and business decision support
  • Advanced expertise in survival analysis, including time-to-event models and censoring
  • Strong capabilities in statistical modeling, forecasting, experimental design, and validation
  • Proficiency in Python software engineering, including modular, tested, well-typed, readable code
  • Experience maintaining and refactoring large shared codebases
  • Experience designing and operating systems of interacting models, including ensembles or chained predictions
  • Deep proficiency in Python and SQL
  • Extensive practical experience with modern modeling and experimentation frameworks
  • Thorough knowledge of statistical methods, predictive algorithms, survival analysis, time-series forecasting, experimental design, measurement, and validation
  • Experience building and maintaining interconnected production models with MLOps practices such as feature stores, training and inference pipelines, workflow orchestration, version control, and post-deployment monitoring
  • Ability to estimate the prospective value of modeling initiatives and assess model performance and business impact after deployment
  • Strong communication and relationship-building abilities
  • Demonstrated ability to influence priorities and technical direction across related workstreams while remaining responsible for hands-on delivery
  • Ability to direct technical work, coach data scientists, and create reusable modeling, experimentation, validation, or reporting practices
  • Familiarity with customer lifetime value forecasting, simulation workflows, forecast-versus-actual analysis, or causal inference
  • Experience in insurance or regulated financial products
  • Experience with cloud data and machine learning platforms and tools such as AWS, Docker, dbt, Airflow, Metaflow, Step Functions, or MLflow
  • Experience creating visualizations, dashboards, or reports
  • Experience prototyping new modeling techniques or data science tools
  • Must appear on camera for virtual interviews

लाभ

  • Competitive bonus opportunity
  • Equity offering
  • Flexibility to work from any location across the United States
  • Reasonable accommodation throughout the hiring process

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