Director - Applied Analytics

About the Role

We're a large insurance brokerage sitting on a vast amount of data: policy, claims, submission, market, and client data across multiple lines of business. We're hiring a Senior Director of Applied Analytics to transform this data into tangible business value—whether through new revenue streams, cost reductions, or organic growth.

This is a hands-on building role first, evolving into a leadership position as the function grows. Analytics and Data Engineering report to the same leader, so you'll collaborate closely with the data engineering team from day one. You'll identify high-impact opportunities, build and deploy solutions, and take ideas from conception to production. Once you've demonstrated success, you'll hire and lead a team. We're looking for someone who thrives on both building and leading.

Responsibilities

• Find and deliver value in three ways:
  • Monetization: Turn data into revenue-generating or cost-saving products, such as benchmarking tools, packaged data products, or internal/external analytics offerings.
  • Operational efficiency: Use data to streamline business operations, reduce manual work, accelerate processes, and empower teams to make faster, smarter decisions.
  • Organic growth: Build analytics to drive growth through cross-sell, improved retention, higher win rates, and an enhanced client experience.
• Lead the building:
  • Identify gaps where data doesn’t exist and work with engineering and operations to capture it, turning today’s gaps into tomorrow’s assets.
  • Develop AI-first data applications and tools that go beyond dashboards, leveraging AI to build faster and integrate AI capabilities into products.
  • Own solutions end-to-end, from ideation and modeling to testing and production on Snowflake and Azure.
  • Collaborate with Data Engineering to establish shared standards for data models, pipelines, and production workflows to ensure reusability and scalability.
• Set direction and grow the function:
  • Translate opportunities into clear, actionable proposals for leadership to fund.
  • Start by building solutions yourself, then scale by hiring, mentoring, and leading a team of analysts, data scientists, and analytics engineers.
  • Demonstrate the impact of your work with measurable business metrics and report progress to leadership.
  • Ensure all solutions adhere to data governance, client confidentiality, and insurance industry regulations, including handling PII and PHI.

Qualifications

• 10+ years in analytics, data science, or similar roles, with a balance of hands-on building and people leadership. You must be a builder who can also lead—able to design and ship solutions independently while growing into a leadership role.
• A proven track record of using data to drive measurable outcomes in revenue, cost, or growth—not just reports or dashboards.
• Experience setting data strategy, including identifying missing data and establishing new collection methods to achieve specific goals.
• Experience building data applications or products that deliver data to business users in a usable, impactful way.
• Hands-on experience with Snowflake and the Microsoft Azure ecosystem, including tools like Azure Data Factory, Azure Storage, and Azure ML or Databricks.
• Strong analytics engineering skills: proficiency in SQL, dimensional and semantic modeling, and Python or R, with a focus on production-grade analytics.
• General software and web development experience, with an understanding of modern application design, development, and deployment. You should be able to build data applications, not just models and pipelines.
• Hands-on experience with AI and machine learning, with a forward-looking perspective on AI-first development. You’ll be expected to use AI tools to accelerate development and integrate AI into products.
• Ability to articulate data opportunities in business terms and defend plans to senior leadership.
• Comfort with ambiguity—able to navigate uncharted territory and create paths where none exist.

Nice to Have

• Experience in insurance, brokerage, financial services, or another regulated, data-heavy industry.
• Familiarity with insurance data, such as policy, claims, submissions, and carrier or market data, as well as agency management systems.
• Experience building data products or analytics-as-a-service for clients.
• A background in data governance, privacy, or compliance, particularly as it relates to data usage.
• An MBA or advanced degree in a quantitative field (equivalent practical experience is equally valued).

What Success Looks Like

• First 90 days: Understand the data landscape and business priorities. Collaborate with Data Engineering to create a ranked list of opportunities across monetization, efficiency, and growth, including areas where new data collection is needed.
• 6 months: Deliver at least one high-value solution into production, establishing a model for collaboration between analytics and engineering.
• 12 months: Demonstrate a portfolio of solutions with measurable business impact, using these results to begin building and scaling your team.

Benefits

• Medical, Dental, and Vision insurance
• Life and AD&D insurance
• Flexible Spending Account (FSA) / Health Savings Account (HSA)
• Commuter & Child Care FSA
• Cancer Support Benefits
• Pet Insurance
• Accident & Critical Illness Insurance
• Hospital Indemnity
• Employee Assistance Program (EAP)
• 11 Paid Holidays
• Flexible Paid Time Off (PTO)
• 401K