Data scientist, pricing and underwriting analytics

A leading organization in a regulated financial services environment is hiring data scientists to build and deliver analytical solutions that directly support sales and underwriting decisions. This position is for an experienced practitioner who can independently manage multiple deliverables, produce accurate outputs under real-world constraints, and turn analytical logic into maintainable tools that business teams use in day-to-day decisioning.

Responsibilities

• Build and maintain predictive models that support underwriting decisions, risk selection, and pricing adequacy
• Develop analytics for sales performance, including segmentation, conversion modeling, revenue forecasting, and opportunity scoring
• Perform portfolio and book-of-business analysis, including profitability drivers, retention signals, loss trends, and mix-of-business insights
• Create automated operational reporting, including recurring dashboards and trusted reporting outputs
• Deliver accurate ad hoc analysis to answer time-sensitive questions from sales and underwriting stakeholders
• Implement analytical applications in Python or R that operationalize business logic, not just standalone models
• Translate actuarial and financial rules into testable, version-controlled code that can be reviewed and audited
• Design and implement decisioning logic such as pricing engines, risk scoring tools, portfolio optimization routines, and simulation frameworks
• Build rule-based components that reflect underwriting guidelines, rate adequacy checks, and approval workflows
• Document assumptions, limitations, and model behavior clearly for business stakeholders and peer review
• Validate messy, multi-source datasets, surface data gaps early, and ensure outputs remain reliable under changing inputs

Required experience and skills

• Production-level programming in Python or R, including clean, reproducible, version-controlled development practices
• Advanced SQL skills for querying large, complex datasets, including multi-table joins across multiple source systems and performance-aware querying
• Applied machine learning experience across classification, regression, clustering, and time-series forecasting, with measurable business outcomes
• Strong statistical foundations, including hypothesis testing, confidence intervals, experimentation concepts such as A and B testing, and generalized linear models
• Demonstrated ability to build working analytical tools and applications used by business teams, not only exploratory notebooks
• Strong data wrangling capability with incomplete, inconsistent, or multi-source data, including validation and quality checks
• Experience building dashboards and reports using Streamlit or a comparable modern Python-based framework
• Strong communication skills, including explaining outputs, uncertainty, trade-offs, and recommendations to non-technical decision-makers
• Proven ability to manage multiple concurrent deliverables without sacrificing accuracy
• Meaningful hands-on experience delivering data science solutions in financial services (insurance, banking, lending, or similar regulated environments)
• Working familiarity with financial performance concepts such as loss ratios, conversion rates, premium volume, and risk appetite (or equivalent metrics)

FAQ

1. What are the core responsibilities of a Data Scientist in pricing and underwriting analytics?
This role focuses on building models and analytical frameworks to support pricing strategies and underwriting decisions. It involves analyzing risk, customer behavior, and financial outcomes to optimize pricing and approval processes. The data scientist translates complex data into actionable insights that drive profitability and risk management.

2. What types of problems does this role typically solve?
Common problems include risk scoring, premium pricing optimization, loss prediction, and customer segmentation. The role may also support underwriting automation and fraud detection. Solutions aim to balance revenue growth with risk mitigation.

3. What data sources are used in pricing and underwriting analytics?
Data sources include historical claims, policy data, customer demographics, credit data, and external risk indicators. The data scientist integrates and cleans data from multiple systems to build reliable datasets. Data quality is critical for accurate modeling.

4. What tools and technologies are commonly used in this role?
Common tools include Python, R, and SQL for data analysis and modeling. Machine learning libraries such as scikit-learn, XGBoost, or TensorFlow may be used. Data visualization tools like Tableau or Power BI help communicate results.

5. How is machine learning applied in underwriting and pricing?
Machine learning models are used to predict risk, estimate losses, and optimize pricing strategies. These models help identify patterns and improve decision accuracy. They support more efficient and data-driven underwriting processes.