Senior Data Scientist (Machine Learning & Generative AI)

Merck•Upper Gwynedd Township, PA

21h•$129,000 - $203,100•Hybrid

About The Position

As a Senior Data Scientist, you will leverage your expertise in machine learning model development, generative AI, and software engineering to rapidly prototype and deploy advanced analytics products for our manufacturing division. You will collaborate with cross-functional teams, including IT, engineering, and operations, to design and implement robust advanced analytics solutions at scale within production systems. You will also engage in ad-hoc consulting roles to support various projects across the organization. Our data science team contributes to different project phases, from ideation and business case development, through data discovery and preparation, to model development, prototyping, and facilitating adoption.

Requirements

Extensive knowledge of both traditional supervised and unsupervised machine learning algorithms, as well as familiarity with advanced deep learning architectures.
Proven hands-on experience with Python, including practical skills with libraries such as scikit-learn, Keras, TensorFlow, or PyTorch.
Familiarity with feature engineering and exploratory data analysis for both structured and unstructured data sets.
Knowledge of experiment tracking methodologies and tools to monitor model performance and maintain reproducibility.
Exceptional communication skills to effectively convey complex information to both technical and non-technical stakeholders.
A strong software engineering mindset, emphasizing the importance of producing high-quality code, documentation, and pipelines.
A data-centric mindset, focusing on how data can be leveraged to create actionable insights and drive business value.
Experience working within Agile frameworks and methodologies.

Nice To Haves

Understanding of generative AI including large language models and vision language models, RAG, pre-training, and/or fine-tuning for specific applications.
Experience with agents, agentic AI platforms (CrewAI, LangGraph, LangChain, AutoGen, Semantic Kernel, Bedrock, Strands, etc), agent tooling and protocols (MCP, A2A), and/or AI coding tools (Claude Code, Cursor, Codex, Open Claw)
Familiarity with graph networks, semantic layers, and causal inference.
Familiarity with deep learning applications or inference, computer vision, autoencoders, etc.
Familiarity with modern MLOps tools and best practices for lifecycle management including CI/CD pipelines, containerization technologies such as Docker, and deploying machine learning models in cloud environments such as AWS, Databricks, or similar platforms.

Responsibilities

Independently design and develop innovative quantitative methodologies, leveraging different methods and approaches in machine learning, deep learning, and generative AI, to drive data-driven decision-making and influence key initiatives within the company.
Apply data science, machine learning, and deep learning techniques to create tools for process monitoring, process optimization, and predictive analytics.
Develop end-to-end digital solutions, including automation of data workflows and integration into existing systems.
Engage with customers and stakeholders to understand their needs, requirements, expectations, and potential opportunities, ensuring alignment with business objectives.
Build and disseminate in-depth domain knowledge of emerging trends in one or more sub-specialty areas of data analytics, fostering collaboration with cross-functional stakeholders.
Design, lead, and document the development of analytics applications, platforms, and processes to unlock value through scientific insights.
Stay updated on current best practices, methodologies, and technologies in AI, machine learning, and data science landscape, including compliance and ethical considerations.