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About This Role
The Data Enablement and Analytics (DEA) team within the PAPD (Product, Analytics and Process Development) organization drives PAPD’s digital transformation by making data usable, useful, and impactful in support of our mission of Transforming Therapeutic Molecules into Products for a Diversified Pipeline.
We are seeking a Process Development Engineer III, AI and Data Science to join our Artificial Intelligence (AI) and Advanced Analytics (AA) team in DEA, who pairs deep bioprocess‐engineering expertise with sophisticated AI/ML and Data Science (DS) capabilities to accelerate biologics development and manufacturing.
You will design, implement, and operationalize AI and DS models for upstream (cell\-culture/bioreactor), downstream (purification) operations, Formulation Development and multiple Analytics teams while partnering closely with process\-development, manufacturing\-sciences, and digital teams. You will turn data into prescriptive guidance, deploy production\-grade models, and build innovative AI solutions that enhance process understanding, optimization, and automation.
A Typical Day in the Role of Process Development Engineer III Might Look Like:
- Build and deploy AI/ML\-powered solutions to accelerate our digitalization journey.
- Advance PAPD’s broader AI, DS and related digital\-maturity initiatives.
- Collaborate with process engineers, citizen data scientists, IT, and manufacturing colleagues to coordinate AI and Advanced modeling efforts enterprise wide.
- Explore, prototype and implement GenAI approaches and solutions (e.g., Retrieval\-Augmented Generation) to enhance knowledge management, and decision support.
- Develop, validate, and maintain mechanistic, hybrid, and data\-driven models for cell culture, purification, formulation and other processes. These include digital twins, advanced predictive modelling, and process control techniques.
- Translate complex bioprocess questions into quantitative modeling strategies that inform scale\-up, tech transfer, and continuous improvement.
- Mentor citizen data scientists and champion best practices in model development, method selection, and code quality.
This Role Might Be For You If You Have:
- Analytical rigor and creative problem solving
- Ability to drive projects autonomously while thriving in cross\-functional teams
- Excellent written and verbal communication
- Passion for innovation and continuous learning
Required Qualifications
- This role requires a Ph.D. in Chemical/Biochemical Engineering, Biotechnology, Applied Mathematics, Computer Science or related field with 0\-2\+ years of industrial experience OR\- Master’s with 7\+ years.
- Expert programming proficiency in Python and experience with statistical/computational tools such as JMP, SIMCA or MATLAB is required.
Proven ability to communicate technical concepts to multidisciplinary stakeholders.
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Preferred Qualifications
- Hands\-on experience with cloud analytics platforms (e.g., Dataiku, Databricks).
- Strong working knowledge of Quality\-by\-Design (QbD) principles and statistically rigorous Design\-of\-Experiments (DoE) for defining design space, optimizing critical process parameters, and informing robust control strategies.
- Familiarity with PAT and chemometric modeling (e.g., Raman spectroscopy) for bioprocess monitoring and control.
- Understanding of operation research techniques such as combinatorial optimization, linear programming, mixed integer programming is a plus.
- Ability to deal with data from both SQL and NoSQL systems to support analytics, real\-time processing, and application performance is a plus.
- Publication record in bioprocess modeling or AI for biomanufacturing is a plus.
- Mechanistic understanding of upstream and/or downstream bioprocess unit operations, scale\-up/down principles, and critical quality attributes is strongly preferred.
- A demonstrated success modeling bioprocesses via first\-principles, hybrid, or data\-driven (ML) methods is preferred.
- A strong foundation in AI/ML algorithms (regression, classification, Bayesian methods, deep learning, time\-series, probabilistic modeling) is a plus, along with expertise in multivariate statistics for process modeling, real\-time monitoring, and control.
- Some experience with GenAI stacks (LLMs, vector databases, RAG pipelines) and multimodal techniques is necessary/required/strongly preferred.
Does this sound like you? Apply now to take your first step towards living the Regeneron Way! We have an inclusive culture that provides comprehensive benefits, which vary by location. In the U.S., benefits may include health and wellness programs (including medical, dental, vision, life, and disability insurance), fitness centers, 401(k) company match, family support benefits, equity awards, annual bonuses, paid time off, and paid leaves (e.g., military and parental leave) for eligible employees at all levels! For additional information about Regeneron benefits in the US, please visit https://careers.regeneron.com/en/working\-at\-regeneron/total\-rewards/. For other countries’ specific benefits, please speak to your recruiter.
Please be advised that at Regeneron, we believe we are most successful and work best when we are together. For that reason, many of Regeneron’s roles are required to be performed on\-site. Please speak with your recruiter and hiring manager for more information about Regeneron’s on\-site policy and expectations for your role and your location.
Regeneron is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion or belief (or lack thereof), sex, nationality, national or ethnic origin, civil status, age, citizenship status, membership of the Traveler community, sexual orientation, disability, genetic information, familial status, marital or registered civil partnership status, pregnancy or parental status, gender identity, gender reassignment, military or veteran status, or any other protected characteristic in accordance with applicable laws and regulations. The Company will also provide reasonable accommodation to the known disabilities or chronic illnesses of an otherwise qualified applicant for employment, unless the accommodation would impose undue hardship on the operation of the Company's business.
For roles in which the hired candidate will be working in the U.S., the salary ranges provided are shown in accordance with U.S. law and apply to U.S.\-based positions. For roles which will be based in Japan and/or Canada, the salary ranges are shown in accordance with the applicable local law and currency. If you are outside the U.S, Japan or Canada, please speak with your recruiter about salaries and benefits in your location.
Please note that certain background checks will form part of the recruitment process. Background checks will be conducted in accordance with the law of the country where the position is based, including the type of background checks conducted. The purpose of carrying out such checks is for Regeneron to verify certain information regarding a candidate prior to the commencement of employment such as identity, right to work, educational qualifications etc.
Salary Range (annually)
$109,900\.00 \- $179,300\.00
Salary Context
This $109K-$179K range is in the lower quartile for AI Product Manager roles in our dataset (median: $189K across 161 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Regeneron, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $213,800 based on 583 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($144K) sits 32% below the category median. Disclosed range: $109K to $179K.
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.
Regeneron AI Hiring
Regeneron has 2 open AI roles right now. They're hiring across Data Engineer, AI Product Manager. Based in Tarrytown, NY, US. Compensation range: $179K - $245K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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