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About This Role
About the Role:
As a Pattern Analyst in LNSSI you will work directly with operational customers and government leadership using advanced analytic methodologies and specialized software against large data sets and complex problems in a detail\-oriented, fast\-paced, time\-sensitive environment. Candidates are expected to possess self\-initiative and strong interpersonal and organizational skills, as well as advanced data handling and technical analysis experience. Candidates must be able to operate professionally and productively, often without direct supervision, while conducting high\-quality analysis, data development, capability briefs, and reach\-back support seamlessly across diverse analytic and technical teams in support of a shared mission.
Responsibilities
- Rigorously investigating, evaluating, interpreting, and delivering data\-driven findings from relevant and diverse data sources.
- Preparing and delivering timely and actionable tactical analysis products tailored to the needs of the customer mission.
- Transforming and analyzing raw data into actionable findings, and communicating through charts, maps, timelines, and infographics to visualize complex relationships and big data sets in a user\-friendly and easy\-to\-understand format to support operational decision making or criminal prosecution.
- Learning, applying, and maintaining relevant subject matter expertise in one or more technical areas in support of customer missions, through analytic work, operational tactics, data handling, methodology development, command briefings, and training and consultation:
- Pattern analysis or tactical crime analysis
- Device geolocation or cell phone analysis and investigation
- Specialized technology in criminal investigations or military operations
- Expert witness court testimony
- Data science analytic methodology
- Software and tools development
- Learning and leveraging a variety of commercial and proprietary software, tools, techniques, and data sources at a proficient level to develop effective analytic solutions regardless of the availability or limitations of a particular tool or dataset.
- Attaining proficiency in LexisNexis Accurint® Trax™, ESRI ArcPro or ArcGIS, i2 Analysts Notebook, Python, Anaconda, or Jupyter Notebook, and proprietary LexisNexis software and databases.
- Rapidly responding to changing circumstances requiring flexibility, time\-management, and the ability to function under shifting priorities while keeping track of ongoing requirements and deadlines.
- Communicating and collaborating on analysis with both internal and external analytic colleagues and with operational customers, in person or remotely, and interacting effectively with command\-level and senior leadership.
- Developing and maintaining relationships with customers on best practices, training, procedures, and techniques to actively drive customer success.
- Supporting sales and marketing engagements with potential customers by demonstrating unique capabilities and developing customized analytic solutions for diverse mission sets.
- Peer mentoring and interactive cross training with team members to share and develop diverse expertise to support multi\-disciplinary solutions.
Requirements
- Must be a US citizen.
- Top Secret security clearance with SCI eligibility required. A new clearance investigation may be sponsored for highly qualified candidates.
- CJIS certification required, as described below\*.
- Bachelor’s degree required. Master’s degree is desirable.
- Minimum 3\-5 years of experience in a government employee or contractor role serving in a rigorous analytic or investigative environment.
- Ability to attain LexisNexis (formerly Zetx) Subject Matter Expert (SME) Certification in Cellular Technologies.
- Ability to understand and adhere to government contract requirements, procedures, and compliance training, often without direct supervision.
- Ability to work up to full time at a secure government site in the Washington DC/Northern Virginia/Maryland area.
- Hybrid to 100% in person position.
- Valid US passport may be required, as up to 25% domestic and international travel required
Preferred Qualifications:
- Documented experience working within operational, analytical, or intelligence teams in law enforcement, the military, or the federal government.
- Operational, investigative, or analytic experience with cell phone and GPS data analysis, geospatial mapping, data science, criminal investigations, court testimony and patterns of human movement
- Industry recognized certifications and professional organization memberships that support and develop relevant subject matter expertise.
\&\#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;\&\#xa;\&\#xa;This job is eligible for an annual incentive bonus.\&\#xa;\&\#xa;
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Salary Context
This $104K-$174K range is below the median for Data Scientist roles in our dataset (median: $158K across 220 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 3,963 AI roles we're tracking, Data Scientist positions make up 7% 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 $198,065 based on 761 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($139K) sits 29% below the category median. Disclosed range: $104K to $174K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; 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 VA, US. Compensation range: $174K - $174K.
Location Context
Across all AI roles, 15% (593 positions) offer remote work, while 3,349 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,103 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $200,000. Top-quartile roles start at $253,000, 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 $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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