Data Product Manager
New
K
KinInsurance data
For all other positions, these roles can sit in any of the following 41 states: AL, AR, AZ, CA (exempt only), CO, CT, FL, GA, ID, IL, IN, IA, KS, KY, LA, MA, ME, MD, MI, MN, MO, MT, NC, NE, NJ, NM, NV, NY, OH, OK, OR, PA, SC, SD, TN, TX, UT, VT, VA, WA, and WI. For remote technical positions located in Canada, we are only able to hire individuals who reside in Ontario.Full-TimeMiddle
Salary$118K - $136K; $118K – $136K • Offers Equity
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Job Details
- Experience
- 2 to 5 years in product management, data analytics, or an analytical role close to the insurance business
- Required Skills
- SQLArtificial IntelligenceProduct ManagementDatabricksLookerData analytics
Requirements
- Have 2 to 5 years of experience in product management, data analytics, or an analytical role close to the insurance business.
- Bring business judgment about how data informs decisions, ideally from analyst experience making recommendations.
- Use advanced SQL to pull and join data across multiple sources independently.
- Be comfortable with a modern data stack such as Databricks and Looker.
- Dig into the underlying question rather than taking the initial request at face value.
- Define metrics precisely, especially when they inform pricing or claims decisions.
- Use AI to improve personal throughput and prototype ideas quickly.
- Have experience partnering with engineering to scope and ship work.
- Bring familiarity with insurance or a clear appetite to learn the business quickly.
- Communicate clearly and concisely with technical and business audiences.
- Bonus: Bring InsurTech or FinTech experience, familiarity with homeowners insurance, or knowledge of loss ratio, premium, underwriting, and catastrophe risk.
- Bonus: Be familiar with Snapsheet, Xactimate, or catastrophe modeling from RMS and Verisk.
Responsibilities
- Identify the underlying decision behind requests from pricing, underwriting, actuarial, and claims teams.
- Shape datasets and data products that support insurance decisions, including rate reviews and claims workflows.
- Connect data across claims systems such as Snapsheet and Xactimate and vendor loss-modeling sources such as RMS and Verisk.
- Build supporting datasets proactively so teams can investigate metrics and problems without waiting for a new data pull.
- Scope and deliver data products in partnership with data engineering.
- Own the data layer as the source of truth for core insurance metrics such as loss ratio and pricing accuracy.
- Define metrics before they ship and improve data quality and instrumentation.
- Translate multi-source data into clear stories and actionable recommendations.
- Partner with pricing, actuarial, underwriting, claims, and data engineering teams.
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