Interested in this Data Scientist role at LexisNexis Risk Solutions?
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About This Role
Are you passionate about building advanced data\-driven solutions that shape critical business decisions?
Do you enjoy leading complex analytics projects and mentoring others to deliver impactful outcomes?
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 https://risk.lexisnexis.com/insurance.
About our Team
We are the Attributes Team within Risk Solutions, responsible for driving the data and analytics foundations that power our core products. By leveraging advanced technical skills, statistical expertise, and data mining techniques, our team builds, optimizes, and scales high\-performance data features and attributes. We transform vast amounts of complex raw data into actionable, model\-ready insights that fuel decisioning engines across the business. Collaboration is at the heart of what we do. We partner closely with key stakeholders across business, technology, and analytics teams to solve complex technical and strategic challenges. Above all, we pride ourselves on a strong 'One Team' culture grounded in deep ownership, decisive problem\-solving, and continuous innovation.
About the Role
This position conducts complex research, design, and attribute development assignments for a product with Attribute team, Modeling, Technology, and Internal Customers. This position should have the ability to lead a project independently, from design to delivery, as well as mentor junior team members. Tactically, this position codes, deploys, troubleshoots, and validates the attributes, as well as maintains and monitors attributes once programmed. The Sr. Data Scientist will also investigate, resolve, and document any data or code issues found.
Responsibilities
- Lead Attribute development design meetings, as well as develop and maintain attributes using KEL from various data sources by working with Modelling, Technology, Product and Internal Customers.
- Validate the data readiness and mentor junior team members in understanding the high\-level project design.
- Test attributes independently for quality, completeness and consistency through constant monitoring and auditing for project of high complexity, using required techniques and tools such as Python, ECL.
- Successfully convert analytic programming code (Including but not limited to: Python, ECL, R) to KEL independently.
- Resolve complex issues as necessary and maintain up to date and accurate documentation of all attributes and attribute sets.
- Define and oversee development and enhancements to Attribute development application and ensure its proper operation. Maintain user guides and training materials for the application.
- Train internal customers as necessary on the development and use of new attributes.
- Mentor junior analysts on proper attribute creation methodologies and optimization techniques.
- Work with other programmers to ensure new attributes and models are implemented accurately in production environment.
- Keep Manager informed of latest progress through effective communication of status and next steps.
Requirements
- Experience leading complex data science or attribute development projects independently
- Strong programming skills in Python, ECL, R, or similar languages
- Ability to design, deploy, troubleshoot, and validate data attributes
- Experience working with large and diverse data sources
- Strong problem\-solving skills and ability to resolve complex technical issues
- Experience mentoring or supporting junior team members
- Ability to communicate progress, status, and next steps effectively
- Familiarity with analytics workflows and production implementation processes
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.
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Salary Context
This $95K-$158K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At LexisNexis Risk Solutions, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($127K) sits 34% below the category median. Disclosed range: $95K to $158K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
LexisNexis Risk Solutions AI Hiring
LexisNexis Risk Solutions has 1 open AI role right now. They're hiring across Data Scientist. Based in Alpharetta, GA, US. Compensation range: $158K - $158K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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