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About This Role
Lead Data ScientistReady to turn bold ideas into real\-world impact?
At Genpact, we don’t just adapt to change, we lead it. AI and digital innovation are transforming the way businesses work, and we’re at the forefront of it. Genpact’s AI Gigafactory, our industry\-first accelerator, exemplifies how we scale advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. Whether tackling complex challenges through large\-scale models or agentic AI, our breakthrough solutions tackle companies’ most complex challenges.
If you thrive in a fast\-moving, innovation\-driven environment, love building and deploying cutting\-edge AI solutions, and want to push the boundaries of what’s possible, this is your moment.
Genpact (NYSE: G) is an agentic and advanced technology solutions company. We leverage process intelligence and artificial intelligence to deliver measurable outcomes. With a strong partner ecosystem and decades of client trust, we provide innovative solutions that transform how businesses run. Powered by a team with an active learning mindset and client centricity at its core, we deliver lasting value for the world’s leading enterprises.
Get to know us at genpact.com and on LinkedIn, YouTube, X, and Facebook.
Job Description
Inviting applications for the role of Lead Data Scientist!
In this role will work extensively in the life sciences analytics space with hands on experience in Pharma Data specialised in field commercial data and CRM analytics
Responsibilities:
- Manage project deliverables negotiate timelines with stakeholders and prioritize tasks effectively.
- Strong understanding of Salesforce hierarchy data alignment and especially IMS/ IQVIA datasets
- Develop KP Is and analytical frameworks with and end\-user mindset.
- Excellent written and oral communication skills and ability to express complex technical concepts effectively both verbally and in writing.
- Preparing status reports (internal and external) by gathering analysing and summarizing relevant information.
- Act as liaison between business and development team to ensure proper execution of proposals in customer required timelines.
- Work with the governance council to establish reporting operation success metrics SL As and related KP Is
- Identify opportunities for integration AIML predictive insights in existing dashboards and reports and drive adoption
- Integrate and analyze multiple data sources including apld claims NPA Personal and Promotional datasets to build robust inputs for Market Mix model and other advanced analytics models.
- Generate reports using data assets that generates insights on testing rates brand performance and patient or HCP engagement metrics.
- Automate and streamline reporting processes to improve efficiency and enable near real\-time performance tracking across marketing initiatives.
- Conduct ad\-hoc analyses to answer business questions related data deep to data questions for sales and marketing activities.
- Collaborate with cross\-functional teams including brand media and analytics partners.
- Interact regularly with clients and stakeholders through calls emails and presentations to communicate data insights performance reports and provide data\-driven recommendations.
- Ensure high quality and accuracy in all model inputs analytical outputs and reports through rigorous data validation and QA processes.
- Demonstrate proactive ownership and problem\-solving skills working independently to identify opportunities.
Qualifications
Bachelors \- Business Analytics, Bachelors \- Computer Science, Bachelors \- Statistics, Masters \- Data ScienceCertifications
Data Science Using R \- SimpliLearnSimpliLearnRequired Skills
Adaptive Technology, Adaptive Technology, Agile Methodology, Amazon SageMaker, Anticipatory Capability, Artificial Intelligence (AI), Asset Management, Automated Machine Learning (AutoML), CI/CD, Client Relations, Collaboration Tools, Computer Vision, Context Awareness, Continuous Delivery, Continuous Integrations, Continuous Testing, Data Provisioning, Data Validation, Design Thinking, Embedded Systems, Executive Presence, Inclusion, Internet of Things (IoT), Machine Learning Model Management, Model Validation {\+ 8 more}Language
EnglishLanguage Proficiency \-
Proficient \- C2Additional Job Location \-
Job Type
RegularMaster Skill List \-
Advanced Analytics / AI / MLRemote Type \-
OfficeWork Shift \-
Day Job (United States of America)The approximate annual base compensation range for this position is:
60,000 to 75,000 USD
“Los Angeles, California based candidates are not eligible for this role. New York area candidates are eligible for this role only.”The actual offer, reflecting the total compensation package plus benefits, will be determined by a number of factors which include but are not limited to the applicant’s experience, knowledge, skills, and abilities; geographic location; and internal equity.
Why join Genpact?
- Lead AI\-powered transformation – Drive innovation and solve real\-world business challenges that matter
- Make an impact – Help global enterprises solve business challenges that matter
- Accelerate your career – Gain hands\-on experience, mentorship, and world\-class learning opportunities to stay ahead
- Work with the best – Join 140,000\+ bold thinkers and problem\-solvers who push boundaries every day
- Thrive in a values\-driven culture – Our courage, curiosity, and incisiveness \- built on a foundation of integrity and inclusion \- allow your ideas to fuel progress
Come join the 140,000\+ coders, tech shapers, and growth makers at Genpact and take your career in the only direction that matters: Up.
Let’s build tomorrow together.
Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation.
Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a 'starter kit,' paying to apply, or purchasing equipment or training.
Salary Context
This $60K-$75K range is in the lower quartile 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 Genpact, 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 ($67K) sits 66% below the category median. Disclosed range: $60K to $75K.
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.
Genpact AI Hiring
Genpact has 6 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in New York, NY, US. Compensation range: $75K - $300K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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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