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?
About the 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 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 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
U.S. National Base Pay Range: $95,300 - $158,800. Geographic differentials may apply in some locations to better reflect local market rates. This job is eligible for an annual incentive bonus.We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120.
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We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.
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Skills Required
- Bachelor's degree in Mathematics, Statistics, Computer Science, Data Science, or other quantitative discipline (or equivalent experience)
- Master's or Ph.D. degree
- Actuarial experience or certification
- 3+ years experience in data manipulation and AI/ML methodologies, preferably using credit data for insurance or financial services
- Strong expertise in one or more of R, Python, or SQL
- Strong programming skills in Python and/or R and experience with pandas, scikit-learn, NumPy, XGBoost, PyTorch, rpart, party, caret, and/or Scala
- Experience manipulating and merging multiple large datasets in a distributed computing environment
- Solid understanding of ML techniques including hypothesis testing, sample design, model development (linear and non-linear), and model validation
- Ability and self-starter attitude to learn new technologies (e.g., Pyspark, ECL, Azure/AWS ML Services) and share cross-functional knowledge
- Cloud experience (Azure/AWS)
RELX Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about RELX and has not been reviewed or approved by RELX.
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Retirement Support — Retirement support is positioned as a meaningful part of total rewards through a 401(k) plan with matching contributions, alongside other financial protections such as life and disability coverage. Tuition reimbursement and share purchase access further broaden the financial value of the package beyond base salary.
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Leave & Time Off Breadth — Leave and time off breadth appears strong, with generous vacation allowances, mental health days, and options like sabbaticals and tiered PTO by tenure. Parental and caregiving leaves are described in detail, reinforcing time-away benefits as a standout component of the overall package.
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Wellbeing & Lifestyle Benefits — Wellbeing and lifestyle benefits are supported by offerings such as mental health support (e.g., app access), EAP resources, gym-related perks, and wellness incentives. Flexible working hours and related work-life supports add to the perceived day-to-day value of benefits.
RELX Insights
What We Do
RELX is a global provider of information-based analytics for professional and business customers across industries. We help scientists make new discoveries, doctors and nurses improve the lives of patients and lawyers win cases. We prevent online fraud and money laundering, and help insurance companies evaluate and predict risk. Our events enable customers to learn about markets, source products and complete transactions. In short, we enable our customers to make better decisions, get better results and be more productive. We do this by leveraging a deep understanding of our customers to create innovative solutions which combine content and data with analytics and technology in global platforms. RELX serves customers in more than 180 countries and has offices in about 40 countries. It employs approximately 30,000 people of whom almost half are in North America. We operate in four major market segments: Scientific, Technical & Medical; Risk & Business Analytics; Legal; and Exhibitions.
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