Sr. Specialist – Business Insights & Analytics, Advanced Analytics - US Vaccines & ID

Merck•Rahway, NJ

4d•Hybrid

About The Position

We are seeking a Business Insights Analytics Sr. Specialist focusing on data science and analytics to join our team. This role will collaborate with the Digital Human Health (DHH) area to utilize data for generating actionable insights. In this role, the Sr. Specialist will act as a strategic thought partner for marketing stakeholders and internal DHH partners actively collaborating and providing data driven insights to help shape commercial decisions. They will partner with cross-functional teams in designing and delivering scalable solutions and valuable insights that address key business questions aligned with business priorities. The Sr. Specialist will partner with cross‑functional teams to design and deliver scalable analytical solutions aligned with business priorities, including the application of Machine Learning, Natural Language Processing, Generative AI (GenAI), and emerging Agentic AI frameworks to accelerate insight generation and automate routine analytical workflows. This may include leveraging LLM‑based retrieval-augmented generation (RAG), autonomous agent pipelines, prompt‑driven analytics, and multi‑agent orchestration to enhance efficiency and analytical depth. The Sr. Specialist may also play a key role in measuring the impact of promotional programs and providing recommendations on how best to optimize future promotional spend through data-driven insights. The Sr. Specialist should possess strong presentation skills, capable of effectively communicating complex methodologies, insights, and results to senior leadership in a concise manner.

Requirements

Bachelor's degree in a quantitative field (Statistics, Mathematics, Computer Science, Data Science, etc.).
3 years of relevant experience delivering complex analytical projects in the pharmaceutical/biotech/healthcare industry.
Proficiency in database management systems and modeling languages preferred (Python, SAS, SQL) as well as expertise with Microsoft Excel and PowerPoint.
Self-motived, proactive mindset, and ability to work independently.
Strong interpersonal and communication skills to interact with diverse teams and stakeholders.
Strong organizational and time management abilities, capable of navigating a complex matrix environment.
Proven data science skills leveraging advanced statistical methods, Machine Learning/Artificial Intelligence, Natural Language Processing (NLP) modeling and model evaluation.
Experience or strong familiarity with Generative AI (GenAI) concepts, including: o Large Language Models (LLMs), prompt engineering, retrieval‑augmented generation (RAG) o Model fine‑tuning, embedding models, vector databases (e.g., FAISS, Pinecone) o Evaluation frameworks for LLM outputs (hallucination detection, grounding, safety)
Exposure to Agentic AI frameworks, such as: o Multi‑agent orchestration frameworks (e.g., LangGraph, LangChain Agents, Semantic Kernel planners) o Workflow/automation agents for data pipelines, monitoring, anomaly detection o Tools enabling autonomous task execution (planning, reasoning, tool‑calling agents)

Responsibilities

Understand the internal stakeholder business needs and priorities to build analyses that promote business objectives by providing actionable insights.
Analyze data using advanced analytical/statistical techniques across disparate databases/sources (including claims/EMR Data and other sources) to develop insights drive to inform the commercial business strategies.
Ensure delivery of high-quality, timely, compliant analyses with excellent user experience (keep clients updated on progress).
Collaborate across DHH teams (i.e. data visualization, data solutions, other data scientists, other BIA teams, etc.) to deliver best-in-class solutions to drive data driven insights often acting in consulting capacity.
Translate complex business questions into specific analytical problems and design scalable solutions.
Understand the complexity/dependencies between multiple teams and platforms (people, technologies).
Engage in continuous learning to stay abreast of emerging trends and methodologies in analytics.
Contribute to innovative ideas and improve efficiency with insights delivery.