At Home Data Scientist Risk Jobs (NOW HIRING)

Are you passionate about using data science to drive smarter risk decisions and create meaningful business impact?

Do you enjoy solving complex analytical challenges, working with large-scale data, and helping teams deliver innovative solutions in a collaborative environment?

Aboutthe Business

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within Insurance, we provide customers with solutions and decision tools that combine public andindustry specificcontent with advanced technology and analytics toassistthem 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 our Team

We are looking for a Sr. Data Scientist I with strong expertise in statistics/modeling and machine learning to join our diverse team of data scientists on the Auto Insurance Rating Analytics team. This individual will play a key role in new product innovation, model development, generating actionable insights, and working closely with the Vertical and Product teams to design and implement new solutions that are cutting edge and support the insurance market.

About the Role

A Senior Data Scientist I should be able to define the scope of a project with support of managers and execute that project independently. Individuals in this role can also support the development and training of junior staff. A Senior Data Scientist I should be self-sufficient in executing basic methods, and work within their teams to execute increasingly sophisticated approaches to deliver outcomes. They should also support the development of best practices.

Responsibilities:

• Developing, analyzing, and modeling operational, economic, management, accounting and other organizational data to quantify the competitive performance of business segments, evaluate potential operational changes, and design new approaches and methodologies
• Analyzing organizational data to recommend solutions to new and complex problems, developing innovative strategies, quantifying the competitive performance of the organization's operations and/or markets; modeling and evaluating the potential impact of changes
• Applying and integrating statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex high-volume data from a variety of sources
• Functional Knowledge: Conceptual and practical expertise in own area required
• Business Expertise: Has knowledge of best practices and how subject matter expertise integrates with others; is aware of the competition and the factors that differentiate the company in the market
• Leadership: Occasionally leads the work of small project teams; provides informal guidance to junior staff
• Problem Solving: Typically resolves problems using existing solutions
• Impact: Works with minimal guidance
• Interpersonal Skills: Explains difficult or sensitive information, models auto insurance risk, particularly in the context of credit-based data sources, generally using GLM techniques

• Supports existing models
• Python experience required
• Cloud experience preferred

• Develops, analyzes and models operational, economic, management, accounting and other organizational data to quantify the competitive performance of business segments, evaluate potential operational changes, and design new approaches and methodologies
• Analyzes organizational data to recommend solutions to new and complex problems, develops innovative strategies, quantifies the competitive performance of the organization's operations and/or markets; models and evaluates the potential impact of changes
• Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex high-volume data from a variety of sources

Requirements:

• Bachelor's degree in Mathematics, Statistics, Computer Science, Data Science, or other quantitative discipline (or equivalent years of experience); Master's/Ph.D. degree preferred. Actuarial experience/certification also preferred.
• 3+ years demonstrated experience in data manipulation and various AI/ML methodologies, preferably in applications using credit data for insurance or financial services
• Strong expertise in one or more of the following: R, Python, SQL, or equivalent analytic software
• Experience manipulating and merging multiple large data sets in a distributed computing environment
• 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
• Strong ability as a self-starter to learn new technologies (Pyspark, ECL, Azure/AWS ML Services) and to share cross-functional knowledge across the teams

Risk benefit statement

Learn more about the LexisNexis Risk team and how we work https://relx.wd3.myworkdayjobs.com/RiskSolutions/page/21c296c982531000b79663f3194b0000

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