Senior Data Scientist (Machine Learning & Generative AI)

About the role

The Senior Data Scientist plays a pivotal role in driving advanced analytics solutions for Merck's manufacturing division. They work closely with IT, engineering, and operations teams to develop and deploy cutting-edge analytics products.

Responsibilities

• Design and develop innovative quantitative methodologies using machine learning, deep learning, and generative AI to support data-driven decision-making.
• Create tools for process monitoring, optimization, and predictive analytics, integrating these tools into existing systems.
• Engage with customers and stakeholders to understand their needs and align them with business objectives, contributing to various projects across the organization.
• Build and disseminate in-depth knowledge of emerging trends in data analytics, collaborating with cross-functional stakeholders.
• Lead the design, development, and documentation of analytics applications, platforms, and processes to unlock value through scientific insights.
• Stay updated on the latest best practices, methodologies, and technologies in AI, machine learning, and data science, including compliance and ethical considerations.

Requirements

• Extensive knowledge of machine learning algorithms, deep learning architectures, and experience with Python, scikit-learn, Keras, TensorFlow, or PyTorch.
• Familiarity with feature engineering, exploratory data analysis, and handling both structured and unstructured data sets.
• Experience with experiment tracking methodologies and tools to monitor model performance and maintain reproducibility.
• Exceptional communication skills to convey complex information to both technical and non-technical stakeholders.
• A strong software engineering mindset with a focus on producing high-quality code, documentation, and pipelines.
• A data-centric mindset, emphasizing the importance of leveraging data to create actionable insights and drive business value.
• Experience working within Agile frameworks and methodologies.

Preferred Qualifications

• Understanding of generative AI, including large language models, vision language models, RAG, pre-training, and fine-tuning for specific applications.
• Experience with agents, agentic AI platforms, agent tooling and protocols, and AI coding tools.
• Familiarity with graph networks, semantic layers, and causal inference.
• Experience with deep learning applications, computer vision, autoencoders, and modern MLOps tools.

Education

A bachelor’s degree is required, with a preferred Ph.D. in chemical engineering, applied mathematics, or a related field with expertise in data science and machine learning projects, or a master’s degree in data science, computer science, applied statistics/mathematics, chemical engineering, or a related field with 2+ years of relevant experience in data science and machine learning projects.