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COMPANY OVERVIEW:

Our client is an insurance technology company that transforms complex, multi-source data into actionable risk intelligence for property and casualty insurers. Its solutions help carriers make faster, more accurate underwriting decisions, reduce premium leakage, improve efficiency, and strengthen portfolio performance.

The company provides unique, proprietary data and analytics to the insurance industry created from the vast experience of the team serving the insurance industry with data and analytics solutions. The experience of the team enables a unique perspective, applying contextual problem solving to identify opportunities to improve and optimize the performance of client processes.

POSITION OVERVIEW:

The Director, Data Science & Analytics is a hands-on analytics leader who owns the quality, predictiveness and continuous improvement of the models and data assets that it delivers to its clients. The role designs and runs client proofs of concept from intake through results, presents findings directly to clients, and translates what is learned in the real world into ongoing product and model improvements.

This position is created to augment the Chief Data Officer (CDO). In the near term the role manages and tracks production data loads and updates while the company completes its data modernization platform; as that platform matures, the balance of the role shifts decisively toward analytics, modeling and client-facing work. The person we hire must be driven and hardworking, adaptable, and ready to live through change as we scale and grow the business.

Reporting Structure

The Director, Data Science & Analytics reports directly to the Chief Data Officer and works day to day with the Chief Technology Officer, who manages the IT and development teams and is delivering the data modernization platform. The role is a core member of the cross-functional product team.

RESPONSIBILITIES:

Analytics, Model Development and Enhancement

• Build, validate, refine and retest the predictive and analytical models on an ongoing basis, continuously improving the data sets and variables used and the quality and predictiveness of the results.
• Establish and run a disciplined model lifecycle: feature engineering, training, validation (lift, gains, stability, bias and drift monitoring), documentation, and controlled release into production.
• Apply current AI and machine learning techniques where they measurably improve outcomes, and evaluate new techniques, tools and philosophies from leaders in the space.
• Test and monitor data quality across all data assets; identify, quantify and drive resolution of quality issues at the source.

Proof of Concept Execution and Client Engagement

• Own end-to-end execution of all client-facing proofs of concept, both standard and custom: intake and inspection of client input files, matching and processing, execution of the jobs that deliver the solution being tested, and analysis of results against the client’s objectives.
• Build automated, repeatable processes for receiving, parsing, inspecting and validating client input so POCs run faster, more consistently and with fewer manual steps each time.
• Summarize and present POC results to clients and prospects in clear, decision-oriented terms; answer technical and methodological questions with credibility and candor.
• Capture what each POC reveals about real-world performance and feed it back into product roadmap decisions and model improvements.

Data Operations (transitional, decreasing over time)

• Manage and track all production data loads and updates in the near term, including scheduling, monitoring, exception handling and communication of status.
• Manage and run designated production jobs and client deliverables, ensuring on-time, high-quality output.
• Partner with the CTO to migrate data-loading and processing work onto the data modernization platform, documenting current processes and validating that the new platform reproduces or improves on today’s results.

Product Team Collaboration

• Serve as an integral member of the cross-functional product team, ensuring new products and enhancements to existing products are designed to be as efficient and high quality as possible.
• Contribute analytical evidence to pricing, positioning and roadmap decisions; benchmark the data and models against competitors and identify opportunities to strengthen competitive advantage.
• Communicate methods, results and trade-offs clearly to non-technical colleagues in sales, customer support and leadership.

Leadership and Growth

• Develop a deep understanding of the information assets, clients, markets and competitive landscape in preparation for broader leadership of the data function.
• Over time, take on ownership of data strategy, information-asset roadmap, partner relationships and the mentoring of additional analytics and data operations staff.
• Model the working culture we want: ownership, urgency, intellectual honesty, and steady execution through change.

PREFERRED PROFILE:

Applied analytics and data science

• Expertise in statistical analysis, experimental design, predictive modeling, and model validation using large, complex, real-world datasets.
• 7+ years of applied analytics or data science experience.
• 3+ years building models used in production or by paying clients.

Machine learning and AI

• Experience with supervised learning, including gradient-boosted trees, GLMs/logistic regression, and regularized models.
• Strong capabilities in feature engineering, model evaluation—including lift, gains, AUC, and stability—and drift monitoring.
• Working familiarity with generative AI and LLM tools for automation and analysis.
• Demonstrated ability to move models from concept through production and improve them over successive releases.

Programming and analytical tools

• Expert-level SQL skills.
• Proficiency in Python, including pandas and scikit-learn or equivalent tools, or R.
• Experience with version control and reproducible, well-documented analytical work.
• Comfortable working hands-on daily and building automation without relying on engineering support.

Data engineering and automation

• Experience designing automated pipelines for file receipt, parsing, validation, and processing.
• Knowledge of ETL/ELT patterns, job scheduling, monitoring, and cloud data platforms.
• 3+ years building or operating production data processes.
• While this is not a pure data-engineering role, the individual must be self-sufficient.

Data quality and governance

• Experience with data profiling, quality metrics, root-cause analysis, data lineage, and documentation of data definitions.
• Working knowledge of compliance considerations affecting consumer and commercial data, including FCRA, GLBA, and state privacy laws.
• Experience working with regulated data is strongly preferred.

Entity resolution and geospatial data

• Experience with address standardization and matching, record linkage, and geocoding.
• Familiarity with consumer, commercial, property, emergency-services, crime, hazard, and regulatory data.
• 2+ years working with address-keyed or location-keyed data.

Insurance and risk-industry knowledge

• Understanding of P&C insurance underwriting, rating, loss experience, and how carriers evaluate and purchase third-party data.
• Familiarity with the insurance data and analytics vendor landscape, including LexisNexis, Verisk, HazardHub, and similar providers.
• 3+ years of experience in insurance, credit, or risk analytics; experience in adjacent regulated-data industries will also be considered.

Client-facing communication

• Experience designing and presenting proof-of-concept results to carrier and partner stakeholders, including executives, actuaries, and analytics teams.
• Ability to write clear summaries and actionable recommendations.
• Regular direct client interaction in previous roles.
• Comfortable explaining and defending analytical methodology to knowledgeable and skeptical audiences.

Project and process management

• Ability to manage multiple concurrent proofs of concept and production commitments.
• Experience documenting and improving repeatable processes.
• Demonstrated success delivering work on time within a small, fast-moving organization.

Leadership potential

• Ability to influence cross-functional colleagues and mentor junior team members.
• Readiness to grow into broader ownership of the data function.
• 2+ years leading projects, workstreams, or small teams.
• Formal people-management experience is not required at the time of hire.

Education

• Bachelor’s degree in statistics, mathematics, computer science, engineering, economics, actuarial science, or another quantitative field.
• Master’s degree or PhD preferred.
• Equivalent demonstrated professional experience will be considered.

Who Succeeds in This Role

• Driven and hard working. Takes ownership of outcomes, not tasks, and brings urgency to both client commitments and long-term model quality.
• Adaptable. Comfortable operating with ambiguity, shifting priorities and evolving platforms as the scales; sees change as the normal condition of a growing business rather than a disruption.
• Innovative and curious. Actively seeks out new techniques, technology and thinking from leaders in data and analytics, and knows how to test an idea quickly before committing to it.
• Intellectually honest. Reports what the data shows, including when a model or POC underperforms, and uses that to make the product better.
• A builder and a communicator. Equally credible writing production-grade code and explaining a lift chart to a carrier executive.
• Ready to lead. Wants to grow into the leadership of the data function and is willing to earn it through hands-on execution first.

What Success Looks Like

First 90 days. Has taken over tracking and management of production data loads with no missed deliverables; has run at least one standard POC end to end and presented results to a client alongside the CDO; has documented the current POC and production processes and identified the top automation opportunities.

First 12 months. Standard POCs run on an automated, repeatable pipeline with materially less manual effort and turnaround time; at least one measurable improvement in model predictiveness or data quality has been released to clients; data-loading work has migrated to the modernization platform with results validated; the role is recognized by clients and colleagues as the analytics authority.

Beyond. Owns the information-asset roadmap and data strategy and is ready to take on additional responsibilities including management.

LOCATION: Remote

Job ID# 3667905

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