Position Summary
The Data Scientist is responsible for applying advanced analytics, statistical modeling, and machine learning techniques to support strategic decision-making across the organization. This role partners closely with stakeholders in Underwriting, Actuarial, Claims, Product, Revenue, and Customer Experience to translate business questions into analytical solutions that improve operational performance, risk evaluation, and customer outcomes.
The Data Scientist develops predictive models, explores new data sources, and generates actionable insights that support pricing, underwriting, claims management, and operational efficiency. This role also contributes to the development and operationalization of models and analytics solutions by working with engineering and product teams to integrate insights into business tools, dashboards, and workflows.
Essential Duties and Responsibilities
- Partner with stakeholders across Underwriting, Actuarial, Claims, Product, and Revenue to identify opportunities where data can inform better business decisions.
- Translate business questions into analytical frameworks, hypotheses, and statistical approaches.
- Analyze customer, broker, and operational data to identify trends, anomalies, and opportunities to improve engagement, retention, and operational performance.
- Conduct exploratory data analysis to investigate business problems and generate actionable insights.
- Communicate analytical findings through visualizations, dashboards, and written summaries.
- Develop predictive models and statistical analyses to support pricing, underwriting, claims, reserving, and operational initiatives.
- Apply machine learning techniques including regression, classification, clustering, and other statistical learning methods to solve complex business problems.
- Evaluate model performance and continuously improve models based on new data and changing business needs.
- Explore internal and third-party data sources to enhance model performance and uncover new insights.
- Collaborate with engineering and product teams to deploy analytical models into production systems and operational workflows.
- Support the implementation of MLOps practices, including model versioning, deployment pipelines, monitoring, and lifecycle management.
- Monitor models for performance degradation, bias, or drift and implement improvements as needed.
- Document model assumptions, methodology, and validation processes to support transparency and governance.
- Contribute to the improvement of internal data infrastructure, ETL pipelines, and reporting architecture.
- Support the development and optimization of dashboards and analytics tools used by business teams.
- Ensure data quality, consistency, and reliability across analytical workflows.
- Identify opportunities to automate recurring analyses and reporting processes.
- Work closely with business stakeholders to ensure analytics solutions align with operational needs and strategic objectives.
- Partner with product and engineering teams to integrate analytical capabilities into internal platforms and decision tools.
- Provide analytical support for ad-hoc requests and strategic initiatives.
- Other duties as assigned.
Qualifications
Education & Experience
- Bachelor's degree in data science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field required.
- Advanced degree (MS or PhD) preferred.
- 3 - 7 years of experience in data science, advanced analytics, or statistical modeling roles.
- Experience working with large datasets and complex analytical problems.
Knowledge, Skills, and Abilities
- Strong programming skills in Python and SQL.
- Experience with statistical modeling and machine learning techniques.
- Experience with data visualization and analytical tools (e.g., Tableau, Power BI, Looker, or similar).
- Ability to translate complex analytical findings into clear business insights.
- Strong problem-solving and critical thinking skills.
- Ability to collaborate effectively with both technical and non-technical stakeholders.
Preferred Qualifications
- Experience working with Property & Casualty (P&C) insurance data, including underwriting, claims, or pricing analytics.
- Familiarity with advanced statistical techniques such as Bayesian modeling or hierarchical models.
- Experience with MLOps frameworks, model deployment pipelines, and model monitoring tools.
- Experience working in cloud data environments (AWS, GCP, Azure).
- Familiarity with data engineering concepts, including ETL pipelines and data warehouse architectures.
- Experience conducting experiments or A/B testing to evaluate business initiatives.
Physical Requirements
This position primarily operates in a professional office or remote environment and routinely uses standard office equipment such as computers and phones. The ability to remain stationary for extended periods and operate a computer is required.
What We Offer:
- Flexible PTO
- Generous parental and caregiver leave
- 401K match
- Excellent medical, dental, and vision plans
- Remote-first culture
- Annual $1000 tuition reimbursement stipend
- And more!
The expected annual salary for this position is between $120,000 and $150,000, with the opportunity to earn an annual bonus of up to 15% of the base salary.