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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:
Data Analytics \& Computational SciencesJob Sub Function:
Data Science Portfolio ManagementJob Category:
ProfessionalAll Job Posting Locations:
Raritan, New Jersey, United States of AmericaJob Description:
About MedTech
Fueled by innovation at the intersection of biology and technology, we’re developing the next generation of smarter, less invasive, more personalized treatments.
Your unique talents will help patients on their journey to wellness. Learn more at https://www.jnj.com/medtech
Role overview
The Commercial Analytics Manager ensures J\&J MedTech’s commercial users have a world\-class analytics and AI experience within Project Butterfly’s Microsoft Dynamics 365 platform. You will bridge business needs and technical capabilities by defining analytics requirements, designing data foundations for AI agents and dashboards, and collaborating with regional and business unit teams to deliver consistent, high\-value analytics solutions.
Define analytics strategy and requirements
- Assess commercial decision processes across sales, marketing, customer service, service \& repair, and professional relations \& operations and define analytics capabilities that enable better, faster decisions.
- Translate business needs into technical requirements for analytics products, including out\-of\-the\-box Dynamics dashboards and custom regional solutions.
- Design the data foundation across L2 and L3 analytical layers that powers dashboards, reports, and AI agents in Butterfly.
- Define standards for metrics, KPIs, and analytical frameworks to ensure consistency across regions and business units.
Enable regional and business unit analytics teams
- Collaborate with regional and business unit analytics leads to understand reporting inventories and standardize where possible.
- Provide guidance, templates, and best practices to ensure regionally authored analytics meet global standards and quality expectations, not least in terms of user experience.
- Facilitate knowledge sharing and community building across the analytics ecosystem.
- Act as the bridge between global data products and regional and business unit analytics consumption needs.
Optimize AI agent and analytics experience
- Ensure the AI agent experience in Dynamics 365 delivers meaningful and accurate insights to commercial users.
- Define feature sets, context, and data structures that enable intelligent, conversational analytics.
- Test and refine analytics user experiences by identifying gaps, usability issues, and opportunities for improvement.
- Partner with Data Product Managers and Data Engineers to ensure analytics requirements are reflected in data product roadmaps.
Required
- Bachelors Degree
- Seven or more years of experience in analytics, business intelligence, or commercial operations with experience defining analytics strategies and requirements.
- Experience designing data models or analytical frameworks that enable self\-service analytics and AI use cases.
- Strong understanding of analytics platforms such as Power BI, Tableau, or similar, and modern data architectures, with the ability to engage credibly with technical teams.
- Deep knowledge of commercial processes and decision making in sales, marketing, or customer service contexts.
- Proven ability to work with diverse stakeholders, including regional teams, business unit leads, and IT, to align on standards and drive adoption.
Preferred
- Hands\-on experience with Microsoft Dynamics 365, CRM or CXM platforms, and embedded analytics capabilities.
- Familiarity with AI agents, Copilot, or conversational analytics tools and their data requirements.
- Technical proficiency with SQL, data platforms such as Databricks, Dataiku, Snowflake, or analytics engineering concepts.
- Background in life sciences, MedTech, or B2B commercial analytics.
- Experience working in global organizations and managing analytics standardization across regions.
- Understanding of data governance, data quality, and their impact on analytics outcomes.
Additional context
We are looking for an analytics strategist and enabler who can define what great looks like, design the foundations to support it, and empower teams across the organization to deliver exceptional analytics experiences.
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.
\#LI\-GR
\#LI\-Hybrid
\#Medtech
Required Skills:
Preferred Skills:
Advanced Analytics, Business Case Modeling, Consulting, Cross\-Functional Collaboration, Data Privacy Standards, Data Science, Data Structures, Digital Fluency, Digital Strategy, End to End Implementation, Global Market, Negotiation, Organizing, Predictive Modeling, Process Improvements, Product Portfolio Management, Technical CredibilityThe anticipated base pay range for this position is :
$137,000\.00 \- $235,750\.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)).
This position is eligible to participate in the Company’s long\-term incentive program.
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 $137K-$235K range is above the 75th percentile 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 ($186K) sits 12% above the category median. Disclosed range: $137K to $235K.
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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