Sr. Data Scientist III (HYBRID)

Senior Data Scientist III
Do you love collaborating with teams to solve complex problems and deliver solutions?
Would you like to partner with the biggest names in the insurance industry?

About the Business

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Insurance vertical, we provide customers with solutions and decision tools that combine public and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. Our insurance risk solutions help drive better data-driven decisions across the insurance policy lifecycle - all while reducing risk. You can learn more about LexisNexis Risk at the link below. https://risk.lexisnexis.com/insurance

About the role

We are looking for a Sr. Data Scientist III to join the Insurance Analytics team with strong expertise in statistics/modeling, machine learning to join our diverse team of data scientists. You will play a key role in new product innovation, model development, generating actionable insights along with working closely with the Vertical and Product teams to design and implement new solutions supporting the insurance market. A Senior Data Scientist III should be able to define scope of a project and execute that project independently. Individuals in this role are expected to support the development and training of junior staff. They develop best practices and are the project leaders

About our Team

The analytics team is responsible for new product innovation, model development, and creating actionable insights for our customers. We work closely with the Vertical and Product teams to design and implement new solutions in the insurance market. By harnessing the power of data, our analytics team empowers insurers to make informed decisions, optimize risk segmentation, and enhance underwriting strategies, ultimately driving success in an ever-evolving insurance landscape.

You will be responsible for:

• Researching and developing new statistical/machine learning models to analyze structured and unstructured data by ideating and experimenting with new methodologies to generate predictive scores and attributes.
• Exploring and mining new data sources to help optimize and validate existing models.
• Leading the design and development of data driven solutions and the development of machine learning/statistical models to build risk segmenting and predictive models.
• Ideating, researching, and designing new and innovative analytics and data science methodologies on large scale and complex data assets.
• Having a thorough understanding of the team's core functions and technologies.
• Working with peers to share subject matter expertise, transfer skills, and develop knowledge base.
• Developing and utilizing innovative strategies to complete business analysis and evaluate the performance of business segments.
• Reviewing data results and communicating findings to stakeholders.
• Enforcing data quality testing best practices.
• Performing all other duties as assigned.

Qualifications

• Have graduate degree (Masters or PhD) in Statistics, Actuarial Science, Data Science, Engineering, Computer Science, or a Quantitative field. Actuarial experience or designation is a plus.
• Have experience in statistical modeling and preferably related to credit or insurance industry.
• Have 5+ yrs. of hands-on statistical model development/machine learning (ML) experience, preferably with at least 2 years' experience related to credit or insurance industry.
• Have solid understanding of ML techniques and statistical methods including hypothesis testing, sample design, model development (linear and non-linear models), validation of machine learning models.
• Have expert programming skills in Python and/or R, with extensive experience with their standard data manipulation and ML packages: pandas, scikit-learn, NumPy, XGBoost, Pyspark in Python and rpart, party, caret in R).
• Have experience with R shiny dashboards.
• Have proven ability as a self-starter to learn new technologies (Pyspark, ECL, Azure/AWS ML Services), programming languages, and to share cross-functional knowledge across the teams.
• Have solid verbal and written communications skills and be comfortable presenting analytical results to senior leadership and business stakeholders.
• Have experience in data management and data analysis in on-premise and cloud database management systems (like SQL Server, Cosmos DB, Blob storage, etc.)
• Have excellent attention to detail, organization, and documentation.

Learn more about the LexisNexis Risk team and how we work here

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