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 healthcare and life sciences. 
• 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.  
• Drive and coordinate incident resolution, root cause analysis, and preventive action implementation.  
• Mentor team members, fostering technical growth and skill development in machine learning, NLP, Computer Vision, and AI research.  
• Foster a culture of continuous learning, staying up to date on AI technologies, and analytics tools, and industry best practices.   

WHAT YOU'LL NEED:

• Master’s degree in Bioinformatics, Statistics, Computer Science, or a related field (PhD preferred). Master’s with 5+ years of relevant experience or PhD with no experience required.  
• Strong foundation in Computer Vision, NLP, Machine Learning and AI principles.  
• Advanced knowledge of statistical techniques, probability, meta-analysis, multivariate calculus, and linear algebra.  
• Strong experience with Python, deep learning frameworks (PyTorch, TensorFlow), and data processing libraries (OpenCV, scikit-learn, pandas). Demonstrated ability to build, fine-tune, and deploy machine learning models, including LLMs, NLP, and predictive analytics solutions.  
• Experience deploying and integrating ML models into production environments.  
• Hands-on experience with AWS (SageMaker, Bedrock), CI/CD pipelines (Octopus Deploy, Git), and infrastructure-as-code (Terraform).  
• Experience in biomedical or healthcare data analysis, integrating multi-modal data (imaging, text, structured data) for comprehensive insights.  
• Experience in prompt engineering, Agentic AI (such as Langchain, MCP, Langraph) and transfer learning techniques for LLMs.   
• Proficiency in data extraction, transformation, and feature engineering from large, complex datasets.  
• Experience with vector databases (e.g., Pinecone, PostgreSQL) for AI applications.  
• Familiarity with Graph Neural Networks (GNNs) or knowledge representation techniques. Experience implementing knowledge graphs and structured data models for AI-driven applications.  
• Ability to design and validate experiments, interpret results, and draw statistically sound conclusions.  
• Proactive, curious, and solution-oriented mindset, with strong ability to prioritize, execute efficiently, and solve complex technical challenges.  
• A proactive and curious mindset, with a willingness to explore innovative solutions.  
• Strong communication and presentation skills, with the ability to collaborate across teams, mentor colleagues, and document methodologies for knowledge sharing.  
• Demonstrated contributions to scientific publications or conference presentations in the life sciences domain.  
• Understanding of data privacy, ethics, and regulatory considerations in life sciences (HIPAA, GDPR, etc.).