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Root Inc.
Root Inc.
1001 – 5000 Darbinieki
ApdrošināšanaAutobūveKonsultācijas
Root Inc. ir patērētāju autoapdrošināšanas uzņēmums, kas, izmantojot mobilo lietotni un viedtālruņa telemātiku, novērtē braukšanas paradumus un nosaka apdrošināšanas polišu cenas. Drošāka braukšana parasti ietekmē apdrošināšanas likmes. Lietotne ļauj saņemt piedāvājumus, pārvaldīt polisi, iesniegt atlīdzības pieteikumus un piedalīties izmēģinājuma braukšanas periodā, kura laikā tiek vākti braukšanas dati. Root arī piedāvā palīdzību uz ceļa un citas apdrošināšanas seguma iespējas daudzos ASV štatos, apkalpojot autovadītājus, kurus interesē uz lietojumu balstīta apdrošināšana.
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Vakances uzņēmumā

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.

USAmerikas Savienotās Valstis
171 400 $ – 214 200 $ / gads
AttālinātiPilna slodzeVadošais speciālistsNepieciešamā Valoda: angļuDatu zinātnieks
AirflowAWSDockerMākonisPythonSQL

Apraksts

  • 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

Prasības

  • 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

Priekšrocības

  • 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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