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About This Role
At Johnson \& Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com
As guided by Our Credo, Johnson \& Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson \& Johnson, we respect the diversity and dignity of our employees and recognize their merit.
Job Function:
Technology Product \& Platform ManagementJob Sub Function:
Technical Product ManagementJob Category:
Scientific/TechnologyAll Job Posting Locations:
Titusville, New Jersey, United States of AmericaJob Description:
Johnson \& Johnson is currently seeking a Lead, Technology Product Management \- AI, Data \& Insights to join our Data \& Insights Product Group located in Titusville, NJ.
Johnson \& Johnson IM Technology (JJIMT), the Enterprise Technology group, supports all business functions under Johnson \& Johnson Innovative Medicine Commercial Pharma business. The Data \& Insights Product Group is uniquely positioned to power world\-class data and analytics technologies that enable end\-to\-end data management, operational/advanced analytics and AI capabilities. By turning our JJIM commercial functions into insight driven organizations that are true analytics competitors, we are redefining how we serve our consumers, patients, healthcare professionals, customers and employees around the world. In close partnership with the full JJIMT organization and our business, we will develop, demonstrate and scale faster and more efficient future\-ready enterprise platforms and capabilities that will help to unlock new and differentiated insights needed to drive innovation, commercial growth, operational efficiencies and improved health outcomes.
Purpose: The Lead, Technology Product Management \- AI, Data \& Insights role blends technical product ownership with hands\-on engineering leadership, focusing on the delivery of scalable AI and GenAI solutions that transform commercial pharma operations. This position will serve as an IT Product Line Lead with strong technical skills, delivering high\-impact analytics products and solutions that address the evolving needs of the Business. The Lead will collaborate with IT peers, External Vendors, Data Aggregator Partners, Business Product Owners, Technology Services, and Finance teams. The person will apply Enterprise technology and development capabilities to deliver solutions that rapidly meet business needs, optimize flow, and enhance our ability to deliver quality products and solutions with greater speed, increased agility and measurable business impact. The ideal candidate will be a technically proficient product leader with direct experience architecting and building AI/GenAI capabilities, including LLMs, AI agents, and cloud\-native data platforms. This role demands fluency in artificial intelligence, data engineering, and cloud technologies, and the ability to translate business needs into impactful, production\-grade solutions. The Lead will be accountable for technical delivery and value realization in partnership with the Business Product Lead.
You will be responsible for:
Product Management
- Define and communicate product vision for AI/GenAI\-enabled analytics platforms. Prioritize features that leverage GenAI to enhance commercial decision\-making.
- Define business value, measurement of return on investment, and realization of business outcomes. Own and manage total cost of ownership, including spending on vendors and services throughout product life cycle.
- Drive and measure adoption, retention, and customer satisfaction of Data Analytics products.
- Serve as Market Research Technology Platform Owner: lead the strategy, design, vendor relationships, integrations, data ingestion (including syndicated and panel data), and analytics enablement for market research platforms to ensure market insights are accurate, accessible, and actionable across commercial functions.
Solutioning \& Delivery
- Serve as Technical Product Owner with hands\-on involvement in building AI/GenAI solutions.
- Architect and implement GenAI capabilities using AWS/Azure stack (e.g., Bedrock, SageMaker, OpenAI, Azure ML).
- Collaborate with data scientists and engineers to build and deploy LLMs, retrieval\-augmented generation (RAG), and agentic workflows.
- Define the standards for the product, including overall design and features, development standards, release management processes and schedules, testing / validation, and compliance.
- Lead product roadmap in collaboration with business owner, manage backlog \& prioritization, enabling capabilities \& new services.
- Develop and lead project plans and all areas related to project management.
- Partner with Technology Services (TS) / vendor teams on demand planning, effort, timing, delivery, and support of new demand.
- Perform source data analysis, data discovery, collaborate with business and peer teams to synthesize the business rules required for data acquisition and creation of information with context association.
- Ensure successful business product owner engagement and completion of user acceptance testing.
- Provide technology leadership and deliver Commercial Data \& Insights solutions for Sales and Marketing, Analytics, Market Access and drive business adoption.
Culture \& CREDO:
- Cultivate collaborative, healthy, inclusive, and credo\-based culture of highly engaged, robust teams by modeling credo values, valuing diverse perspectives, with high\-touch approach to employee talent and development.
- Introduce bold ideas that challenge thinking and ways of working.
- Apply strong problem\-solving skills with ability to accurately analyze situations and reach productive decisions based on informed judgment.
Qualifications / Requirements:
Required:
- Bachelor’s degree in computer science, IT, information systems, statistics, data science, or a related field.
- A minimum of 3 years of diverse experience in designing and implementing data \& insights.
- Exposure to LLMs, AI agents, and GenAI\-powered applications in production.
- Expertise in data management, data modeling, large data lake platforms, and BI visualization technologies such as Tableau and Power BI.
- Strong understanding of prompt engineering, model fine\-tuning, and evaluation metrics.
- A keen interest in staying up to date with latest developments in AI / GenAI space and ready to experiment and innovate with new ideas / solution prototypes
- Executive presence, ease in interacting with senior leaders and strong presentation skills
- Demonstrable experience crafting technical solutions for business problems; understands both business processes and technical solutions.
- Critical thinking skills, creative and flexible problem solving, and process focus.
- Strong interpersonal skills and the ability to communicate effectively, strategically, and authoritatively with internal partners to develop ideas, find opportunities, and influence outcomes.
- Demonstrated ability to exercise good judgment in high pressure, critical situations with attention to detail.
- Consistent record of delivering projects efficiently with ambitious timelines.
Preferred:
- Expertise in Python, ML frameworks (e.g., LangChain), and cloud\-native AI services.
- Pharmaceutical or Life sciences/health care industry experience.
- Familiarity with market research concepts, syndicated market data, survey methodologies, and the technology platforms that support market research workflows.
- Extensive knowledge of commercial pharma business processes like market research, alignment, call planning, sales reporting, Medical Affairs and patient data.
- Experience with wide array of pharmaceutical data including but not limited to sales and marketing syndicated data, CRM, digital, hub, claims, specialty pharmacy, etc.
- Agile development experience, SDLC methodology, risk management including information security and solid understanding of JIRA.
- Hands\-on experience in cloud technologies and expertise in AWS or Azure Tech stack is required.
Other:
- This position's primary location will be in Titusville, New Jersey and requires up to 10% travel to our Raritan NJ, New Brunswick NJ, and Horsham PA offices.
Johnson \& Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.
Johnson \& Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us via https://www.jnj.com/contact\-us/careers , internal employees contact AskGS to be directed to your accommodation resource.
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Required Skills:
Data Insights, Data Solutions, Generative AI, Stakeholder PartnershipsPreferred Skills:
The anticipated base pay range for this position is :
$94,000\.00 \- $151,800\.00
Additional Description for Pay Transparency:
Subject to the terms of their respective plans, employees are eligible to participate in the Company’s consolidated retirement plan (pension) and savings plan (401(k)).
Subject to the terms of their respective policies and date of hire, employees are eligible for the following time off benefits:
- Vacation –120 hours per calendar year
\- Sick time \- 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
- Holiday pay, including Floating Holidays –13 days per calendar year
\- Work, Personal and Family Time \- up to 40 hours per calendar year
- Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
- Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
- Caregiver Leave – 80 hours in a 52\-week rolling period10 days
- Volunteer Leave – 32 hours per calendar year
- Military Spouse Time\-Off – 80 hours per calendar year
For additional general information on Company benefits, please go to: \- https://www.careers.jnj.com/employee\-benefits
Salary Context
This $94K-$151K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Johnson & Johnson, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($122K) sits 26% below the category median. Disclosed range: $94K to $151K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Johnson & Johnson AI Hiring
Johnson & Johnson has 29 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Antonio, TX, US, Spring House, PA, US, Santa Clara, CA, US. Compensation range: $106K - $401K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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