Staff Data Scientist, Life Sciences AI

WHAT YOU'LL DO:

• Leverage machine learning, deep learning, prompt engineering, and data mining technologies to develop AI-driven solutions for life sciences and healthcare use cases.
• Collaborate with domain experts to ensure the relevance and accuracy of data-driven insights.
• Ensure data privacy and security compliance in all data handling and processing activities.
• Evaluate and implement feedback mechanisms to improve AI solutions.
• Develop knowledge graphs and structured data representations to enhance AI-powered insights.
• Develop and maintain data pipelines, integrating multiple data sources, including warehoused and pre-modeled data.
• Interpret and communicate insights and findings through reports, dashboards, and presentations for internal and external audiences.
• Follow software engineering best practices to write clean, reliable, and testable code, supporting rapid delivery via CI/CD and automated deployments.
• Explore new technologies, proof-of-concepts (PoCs), and technical roadmaps.
• Work closely with cross-functional teams to align AI/ML solutions with business needs.
• Estimate technical work for product requests, assisting in roadmap planning and prioritization.
• Champion adherence to technical standards and ensure alignment with architectural direction.
• Identify, track, and minimize technical debt within the team.
• Lead and coordinate incident resolution, root cause analysis, and preventive action implementation.
• Mentor team members, fostering technical growth and skill development in machine learning, NLP, and AI research.
• Foster a culture of continuous learning, staying up to date on AI technologies, analytics tools, and industry best practices.

WHAT YOU'LL NEED:

• Master’s degree in Statistics, Data Science, Computer Science, or a related field; PhD preferred.
• Master’s degree with a minimum of 5 years of relevant work experience; or PhD with no work experience required.
• Strong foundation in data science, machine learning, deep learning, and AI principles.
• Advanced knowledge of statistical techniques, probability, multivariate calculus, and linear algebra.
• Demonstrated experience building, fine-tuning, and deploying machine learning models, including large language models (LLMs), NLP models, and predictive analytics solutions.
• Experience in prompt engineering, Agentic AI (such as Langchain, MCP, Langraph), and transfer learning techniques for LLMs.
• Hands-on experience with deep learning frameworks such as TensorFlow or PyTorch.
• Experience implementing knowledge graphs and structured data models for AI-driven applications.
• Expertise in model versioning, monitoring, A/B testing, and deployment in production environments.
• Strong experience with Python for machine learning, data processing, and full-stack development.
• Hands-on experience with AWS cloud services, including SageMaker, Lambda, Redshift, and infrastructure-as-code (Terraform).
• Experience developing and maintaining MLOps pipelines and integrating ML models into production systems.
• Proficiency in CI/CD pipelines with tools like Octopus Deploy, Git, and automated testing frameworks.
• Proficiency in data extraction, transformation, and feature engineering from large, complex datasets.
• Strong ability to prioritize, execute tasks efficiently, and solve complex technical challenges.
• A proactive and curious mindset, with a willingness to explore innovative solutions.
• Excellent communication skills and presentation skills, with the ability to collaborate across teams and mentor colleagues.
• Ability to document processes, methodologies, and best practices for knowledge sharing.
• Experience with vector databases (e.g., Pinecone, PostgreSQL) for AI applications.
• Familiarity with Graph Neural Networks (GNNs) or knowledge representation techniques.
• Demonstrated ability to contribute to scientific publications in the life sciences or healthcare domain.