Senior Data Scientist (machine Learning & Generative Ai)

Merck Merck · Pharma · NJ

Senior Data Scientist role focused on developing and deploying advanced analytics products, including generative AI and machine learning models, for the manufacturing division. The role involves end-to-end solution development, from ideation to adoption, with a strong emphasis on software engineering best practices and collaboration with cross-functional teams. Experience with agents and generative AI is preferred.

What you'd actually do

  1. 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.
  2. Apply data science, machine learning, and deep learning techniques to create tools for process monitoring, process optimization, and predictive analytics.
  3. Develop end-to-end digital solutions, including automation of data workflows and integration into existing systems.
  4. Engage with customers and stakeholders to understand their needs, requirements, expectations, and potential opportunities, ensuring alignment with business objectives.
  5. 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.

Skills

Required

  • Python
  • scikit-learn
  • Keras
  • TensorFlow
  • PyTorch
  • experiment tracking
  • communication skills
  • software engineering
  • Agile Methodology

Nice to have

  • generative AI
  • large language models
  • vision language models
  • RAG
  • pre-training
  • fine-tuning
  • agents
  • agentic AI platforms
  • agent tooling
  • AI coding tools
  • graph networks
  • semantic layers
  • causal inference
  • deep learning applications
  • inference
  • computer vision
  • autoencoders
  • MLOps
  • CI/CD pipelines
  • Docker
  • AWS
  • Databricks

What the JD emphasized

  • extensive knowledge of both traditional supervised and unsupervised machine learning algorithms
  • proven hands-on experience with Python, including practical skills with libraries such as scikit-learn, Keras, TensorFlow, or PyTorch
  • strong software engineering mindset
  • data-centric mindset

Other signals

  • prototype and deploy advanced analytics products
  • design and implement robust advanced analytics solutions at scale
  • leverage expertise in machine learning model development, generative AI, and software engineering