About the business:

LexisNexis Risk Solutions helps Auto, Commercial, Life and Property insurers improve results with our superior combination of cutting-edge technology, data and advanced analytics. At LexisNexis Risk Solutions, our insurance risk solutions helps our customers to improve rating, underwriting and customer experience capabilities and drive better data-driven decisions across the insurance policy lifecycle – all while reducing risk. In fact, our data and analytics support more than 131 million insurance purchase decisions a year. You can learn more about LexisNexis Risk at  https://risk.lexisnexis.com/. ​

About the team:

Insurance Analytics is a global data science and analytics organization that supports new product innovation and profitability driven decisions across the insurance life cycle (marketing, underwriting/rating, renewal and claim)s. The team develops new analytical driven solutions ranging from predictive algorithms (glms, decision trees) to advanced machine learning models (computer vision, nature language processing) to extract key data and insights that helps automate decisioning, improves customer experience and provide key segmentation and rating elements.

About the position:

We are looking for a Sr. Data Scientist 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 that support the commercial insurance market.  

You'll be responsible for:

This position will work on multiple projects concurrently, will self-manage their tasks with minimal supervision, will be asked to lead projects, and mentor junior data scientists.

• Research and develop new statistical/machine learning models to analyze structured and unstructured data by ideating and experimenting with new methodologies to generate predictive scores and attributes

• Lead the design and development of data driven solutions and the development of machine learning/statistical models to build risk segmenting and predictive models.

• Ideate, research and design new analytics and data science methodologies on large scale and complex data assets to

• Analyze client data to prepare for analysis. Identifies data quality concerns and opportunities (i.e. performance indicators, scores, approval/declination indicators, etc.) and clearly communicates these concerns to stakeholders.

• Executes database queries to extract and aggregate LN’s data assets into attributes that are used for predictive modeling, reporting and analysis

• Store, archive, index, transfer and analyze large and complex datasets (structured and unstructured data)

• Creates and enhances Python/R or GUI-based reporting tools that allow team members to gain insights about LexisNexis data assets

• Explore and mine new data sources to help optimize and validate existing models.

• To review data results and communicate findings to stakeholders

• Enforces data quality testing best practices

Qualifications:

• Solid understanding of ML techniques including hypothesis testing, sample design, model development (linear and non-linear models), validation of machine learning models.

• Strong programming skills in  Python and/or R, with extensive experience with their standard data manipulation and ML packages: pandas, scikit-learn, NumPy, XGBoost, PyTorch in Python and rpart, party, caret in R) and/or Scala. Experience with R shiny dashboards.

• Strong 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.

• Strong verbal and written communications skills and is comfortable presenting analytical results to senior leadership and business stakeholders.

• Experience in data management and data analysis in on-premise and cloud database management systems (like SQL Server, Cosmos DB, Blob storage, etc.)

• Excellent attention to detail, organization, and documentation.

• Comfortable working in a fast paced environment.

• Graduate degree (Masters or PhD) in Engineering, Computer Science, Data Science, or a Quantitative field.

• 5+ yrs. of hands-on statistical model development/machine learning (ML) experience

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

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