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About This Role
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process outsourcing services, dedicated to helping the world's leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.). Cognizant is a member of the NASDAQ\-100, the S\&P 500, the Forbes Global 1000, and the Fortune 500 and we are among the top performing and fastest growing companies in the world.
Lead Data Scientist role in a global enterprise focusing on advanced Agentic AI NLP open source LLMs and Generative AI. The role designs and delivers scalable AI solutions for hybrid work environments enabling responsible innovation that improves business outcomes and creates meaningful impact on customers and communities.
Responsibilities
Drive end to end design of Agentic AI solutions that orchestrate autonomous workflows and deliver measurable business outcomes for global stakeholders in a hybrid work environment.
Develop robust NLP models that extract insights from large scale multilingual text data to improve decision making and enhance customer experience across multiple channels.
Architect and implement open source LLM based solutions that are optimized for latency scalability and cost efficiency while maintaining strong performance on enterprise use cases.
Guide design of generative AI applications that create high quality content recommendations and summaries while adhering to responsible AI and data privacy standards.
Establish reusable frameworks prompts and evaluation methods that enable teams to rapidly prototype and productize Agentic AI and generative AI capabilities.
Coordinate with product managers architects and engineering teams to translate complex business needs into technical AI roadmaps and backlog items with clear acceptance criteria.
Review data pipelines feature stores and model serving infrastructure to ensure that AI solutions remain reliable observable and maintainable in production environments.
Perform rigorous experimentation benchmarking and error analysis on LLMs and NLP models to continuously improve accuracy robustness and fairness across user segments.
Document solution designs experiment findings model cards and operational runbooks in a clear and comprehensive manner to support maintainability and audit readiness.
Collaborate with risk legal and compliance partners to embed governance model monitoring and ethical AI practices throughout the solution lifecycle.
Mentor senior data scientists and machine learning engineers through code reviews design discussions and knowledge sharing on Agentic AI NLP and LLM best practices.
Engage with business stakeholders to explain complex AI behaviors manage expectations and ensure that deployed solutions create transparent value for the organization and society.
Evaluate emerging open source LLMs frameworks and tooling to recommend pragmatic adoption paths that align with enterprise security cost and performance requirements.
Qualifications
Exhibit extensive hands on experience in designing and deploying Agentic AI systems that coordinate multiple tools or services to complete complex business tasks.
Demonstrate strong expertise in modern NLP techniques including transformers semantic search text classification summarization and conversational modeling.
Show proven track record in implementing and fine tuning open source LLMs using approaches such as instruction tuning parameter efficient methods and retrieval augmentation.
Apply deep understanding of generative AI concepts such as prompt engineering alignment content filtering and evaluation of generated text for quality and safety.
Utilize solid programming skills in languages like Python along with experience in machine learning libraries and deep learning frameworks appropriate for large models.
Employ sound knowledge of data engineering fundamentals including data quality feature engineering and MLOps practices for reliable model deployment and monitoring.
Display experience working in hybrid global teams using modern collaboration tools with the ability to manage priorities across multiple parallel AI initiatives.
Combine strong analytical thinking and problem solving abilities with clear communication skills that make complex AI concepts accessible to diverse stakeholders.
Apply understanding of responsible AI principles including bias detection explainability privacy preservation and human oversight in all AI design decisions.
Maintain awareness of current research and industry trends in Agentic AI NLP LLMs and generative AI and translate relevant advances into practical enterprise solutions.
Application Accepted: 7/30/2026
The annual salary for this position is between $150,000 \- $160,000 depending on experience and other qualifications of the successful candidate.
Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
- Medical, Dental \& Vision Insurance
- 401(k) with Company Match
- Employee Stock Purchase Program
- Paid Time Off \& Holidays
- Paid Parental Leave
- Short\-Term \& Long\-Term Disability Coverage
- Employee Assistance Program (EAP)
- Mental Health \& Wellbeing Programs
- Health Advocacy \& Virtual Care Services
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable la
Our strength is built on our ability to work together. Our diverse backgrounds offer different perspectives and new ways of thinking. It encourages lively discussions, creativity, productivity, and helps us build better solutions for our clients. We want someone who thrives in this setting and is inspired to craft meaningful solutions through true collaboration.
If you are content with ambiguity, excited by change, and excel through autonomy, we’d love to hear from you!
\#LI\-AV1\#CB\#Ind123
Salary Context
This $150K-$160K range is above 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 Cognizant, 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 ($155K) sits 20% below the category median. Disclosed range: $150K to $160K.
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.
Cognizant AI Hiring
Cognizant has 22 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Architect. Positions span Irving, TX, US, Louisville, KY, US, New York, NY, US. Compensation range: $85K - $435K.
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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