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About This Role
Are you excited to shape the next generation of AI‑powered drafting and search experiences at scale?
Do you want to work on advanced Generative AI systems that turn complex information into clear, trusted insights?
About our Team
You will join a multidisciplinary team of data scientists, engineers, and product partners focused on building intelligent, reliable, and responsible AI solutions for legal and business professionals. We work collaboratively, value thoughtful experimentation, and prioritize high‑quality, production‑ready outcomes.
About the Role
As a Senior Data Scientist II , you will design and improve Generative AI, Retrieval‑Augmented Generation (RAG), and agentic RAG systems that power drafting, search, and reasoning use cases. You will focus on retrieval quality, evaluation, and production‑grade machine learning components, working closely with cross‑functional partners to deliver measurable improvements in relevance, accuracy, and user trust.
Responsibilities* Design, build, and optimize Generative AI, RAG , and agentic RAG systems for drafting, search, and reasoning use cases
- Improve LLM ‑generated outputs through prompt design, retrieval optimization, evaluation, and experimentation
- Develop and maintain scalable data science and machine learning components for production environments
- Build and enhance retrieval pipelines, including query processing, document retrieval, ranking, reranking, and grounding strategies
- Apply text search and relevance tuning techniques using platforms such as OpenSearch and Solr
- Use embeddings, hybrid retrieval, semantic search, and reranking methods to improve answer quality and system performance
- Write high‑quality, maintainable, production‑ready Python code following strong software engineering practices
- Collaborate closely with partners across data science, engineering, product, and domain teams
Requirements* Proven experience building and improving Generative AI and LLM‑based solutions in production or near‑production settings
- Strong experience with RAG frameworks , retrieval‑based AI architectures, and agentic workflows
- Advanced Python skills, including modular design, testing, debugging, and clean coding practices
- Strong understanding of information retrieval, text search methods, and retrieval architectures
- Hands‑on experience with OpenSearch, Solr, or similar search platforms
- Experience with embeddings, semantic retrieval, reranking , and relevance optimization
- Proven ability to work effectively in a monorepo environment with disciplined version control and code review practices
- Experience collaborating in cross‑functional teams and contributing to CI/CD and deployment workflows
Work in a Way That Works for You
We promote flexible ways of working that support collaboration, focus, and personal wellbeing, while meeting the needs of our teams and customers.
Working Pattern
This role supports flexible working arrangements, balancing individual productivity with team collaboration.
About the Business
You will be part of the LNLP division, within the Standard business unit, delivering trusted information and technology solutions that help professionals make confident decisions.
\#AIFluent
\&\#xa;\&\#xa;U.S. National Base Pay Range: $104,900 \- $174,700\. Geographic differentials may apply in some locations to better reflect local market rates.\&\#xa;\&\#xa;If performed in Maryland, the base pay range is $110,100 \- $183,500\.If performed in New York, the base pay range is $115,400 \- $192,200\.If performed in New York City, the base pay range is $125,900 \- $209,700\.If performed in Rochester, NY, the base pay range is $104,900 \- $174,700\.If performed in New Jersey, the base pay range is $123,816 \- $197,784\.\&\#xa;\&\#xa;This job is eligible for an annual incentive bonus.\&\#xa;\&\#xa;\&\#xa;\&\#xa;\&\#xa;\&\#xa;
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Salary Context
This $104K-$197K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At LexisNexis Legal & Professional, 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($151K) sits 24% below the category median. Disclosed range: $104K to $197K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
LexisNexis Legal & Professional AI Hiring
LexisNexis Legal & Professional has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Raleigh, NC, US. Compensation range: $192K - $197K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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