kWh Analytics is a leading provider of Climate Insurance for zero carbon assets. Utilizing their proprietary database of over 300,000 operating renewable energy assets, kWh Analytics uses real-world project performance data and decades of expertise to underwrite unique risk transfer products on behalf of insurance partners. kWh Analytics has recently been recognized on FinTech Global’s ESGFinTech100 list for their data and climate insurance innovations.

The Solar Revenue Put production insurance protects against downside risk and unlocks preferred financing terms, and the Renewable Energy Property Product offers comprehensive coverage against physical loss. These offerings, which have insured over $4 billion of assets to date, aim to further kWh Analytics’ mission to provide best-in-class Insurance for our Climate.

The Loss Data Analyst role has the unique opportunity to work broadly across the organization, interfacing with & learning from experts in renewable energy, engineering, data science, underwriting, and risk analysis. We are building the industry’s most advanced models to predict damages to renewable energy plants due to natural catastrophes. When building these models, a large, high-quality data set is key to our success. The Loss Data Analyst will use a wide variety of skills to help us further build out the extent & quality of our loss database, thereby having a significant impact on the accuracy of our risk modeling.

How you will spend your time

• Expand and ensure the quality of our loss database through a variety of avenues:
Data Entry:
• Manually enter claims or damage event data obtained from external parties and from desktop research, including nature of loss, site characteristics at time of loss (e.g. PV module details), dollar amount of loss, days of downtime, etc.
• Identify gaps in data gathering process & propose new data collection processes where necessary
Desktop Research:
• Ensure quality & completeness of loss data for existing and new data points by tracking down and piecing together all available details through a variety of sources, e.g. news articles, PV operating reports, module specification sheets, weather data, social media, etc.
Claims Data Inference:
• Learn to infer and interpret ambiguous details around loss data, including insurance policy structures, loss run histories, and damage amounts, and translate that ambiguity into uncertainty
Field Research:
• Learn about state-of-the-art PV tech while gathering information on industry standards, site resiliency measures, and other property insurance relation information via conferences, calls with experts and asset owners
Data Analysis:
• Provide data summaries including trends in size and quality of database, losses by category or location, and comparisons with industry trends where applicable. Work with the data science team to identify potential model improvement areas based on these insights
New Data Acquisition:
• Search for and coordinate obtaining new datasets that may be useful for data science or underwriting

What you can look forward to

• Working with a talented, experienced, mission-driven team
• Growing and improving the quality of the industry’s largest solar energy database
• Putting on your detective hat to dive into the details of loss events
• Becoming an expert in inferring loss events from multiple, scattered sources of data
• Opportunities to build relationships with external PV industry experts to advance our knowledge of PV property risk and expand our data sets
• Starting a path to a career in data quality analysis, data engineering, data science, business, or assistant underwriting
• An equity stake in the company, via incentive stock options
• A wide variety of medical, dental, and vision plans. 401(k), HSA, FSA and corporate discounts

What you likely need to do the job well

• Ability or aptitude for using insurance & PV intuition to make reasonable assumptions surrounding claims data points
• Must be excited to learn from your expert coworkers how to develop an insurance & PV intuition in order to make reasonable assumptions surrounding claims data points
• Must be comfortable working with many different sources of data, including developing, documenting, and using processes related to the interpretation of that data
• Must be able to structure & organize messy, incomplete data
• Research skills and curiosity - able to dig deep & use multiple channels/sources to understand a data point as much as possible
• Willing & able to reach out to external parties to obtain new datasets or other pertinent information
• Proficient in Excel
• Detail-oriented, motivated self-starter
• 4-year degree in a technical or business field or equivalent experience

Nice to have

• Experience in photovoltaics / solar energy industry
• Experience in property insurance, or related field
• Background in claims inference/analysis, data entry, business analysis
• Familiarity with Python or SQL
• Familiarity with BI tools such as Tableau, Spotfire, or PowerBI
• Associate in Risk Management, Certified Risk Analyst credential, Chartered Enterprise Risk Analyst credential, or other related credentials

Learn more about what we do and why it matters: https://www.kwhanalytics.com/company-overview