AI Product Manager: NLP & GenAI Pipelines

Merck; Rahway (NJ); Hybrid; USD 87,000 - 137,000; Full time

Job summary

Merck in Rahway, NJ seeks a Data Scientist/AI Engineer or AI Product Manager to turn ML, LLM, and Agentic AI prototypes into reliable production services for NLP and unstructured data insights across R&D.

You will partner with scientists, data engineers, and software teams, driving discovery, requirements, stakeholder alignment, and adoption, while advancing product ownership within a cross-disciplinary AI capability team.

Qualifications

• 3+ years of professional experience in data science, AI/ML Engineering, Agentic AI, AI Product Management, or related discipline.
• HS diploma required.
• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Biomedical Informatics, Engineering, or related field, or equivalent practical experience.

Responsibilities

• Partner with business partners and scientists to understand pain points and translate needs into technical user stories.
• Own and prioritize product backlog and roadmap for regular deliveries.
• Write clear requirements, acceptance criteria, and value capture plans.
• Demonstrate features to users, lead enablement sessions, and collect structured feedback.
• Understand structured and unstructured data to address partner needs.
• Collaborate with Data Engineering to productionize prototypes with robust pipelines.
• Establish evaluation metrics to confirm performance for business needs.

Skills

Python

SQL

git

Pandas

LangChain

Hugging Face

spaCy

Retrieval-Augmented Generation

Prompt Engineering

Product Ownership

NLP

Education

Bachelor's degree in Data Science / CS / Math / Biomedical Informatics

HS diploma

Tools

LangGraph

LangChain

Hugging Face

Job description

Merck in Rahway, NJ seeks a Data Scientist/AI Engineer or AI Product Manager to turn ML, LLM, and Agentic AI prototypes into reliable production services for NLP and unstructured data insights across R&D.

You will partner with scientists, data engineers, and software teams, driving discovery, requirements, stakeholder alignment, and adoption, while advancing product ownership within a cross-disciplinary AI capability team.