Data Scientist, Life Sciences AI

Qualifications

• Master's degree in Statistics, Data Science, Computer Science, or a related field;
• 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 Tensor Flow or PyTorch
• Experience implementing knowledge graphs and structured data models for AI-driven
• 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 Sage Maker, 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
• 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, Postgre
• SQL) for AI
• with Graph Neural Networks (GNNs) or knowledge representation techniques
• Demonstrated ability to contribute to scientific publications in the life sciences or healthcare domain.$180,000 - $200,000 a year…

Responsibilities

• In this role, you will design, develop, and optimize machine learning models for real-world life sciences and healthcare applications
• You will work with large, complex datasets, apply cutting-edge machine learning and natural language processing (NLP) techniques, and collaborate with cross-functional teams to integrate AI solutions into our products
• A successful candidate will have experience in end-to-end machine learning model development-from data preprocessing and feature engineering to model training, evaluation, and deployment in production environments
• 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
• 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
• 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
• 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

Full Description

Position: Staff Data Scientist, Life Sciences AI

At IMO Health, a core team of software developers, data scientists, and domain experts combine computer science, healthcare, and life sciences expertise to help professionals access high-quality health information quickly and easily. We need a Staff Data Scientist, Life Sciences AI with a strong background in building and maintaining AI-driven solutions to join this team!

In this role, you will design, develop, and optimize machine learning models for real-world life sciences and healthcare applications. You will work with large, complex datasets, apply cutting-edge machine learning and natural language processing (NLP) techniques, and collaborate with cross-functional teams to integrate AI solutions into our products.

A successful candidate will have experience in end-to-end machine learning model development-from data preprocessing and feature engineering to model training, evaluation, and deployment in production environments. You should be comfortable with cloud-based AI infrastructure, scalable ML pipelines, and best practices in MLOps.

Join our growing Data Science & Analytics department as a Staff Data Scientist, Life Sciences AI to drive AI-powered innovation that advances biomedical research, clinical development, and real-world evidence.

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

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

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

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 Tensor Flow or PyTorch.

Experience implementing knowledge graphs and structured data models for AI-driven

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 Sage Maker, 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

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, Postgre

SQL) for AI

with Graph Neural Networks (GNNs) or knowledge representation techniques.

Demonstrated ability to contribute to scientific publications in the life sciences or healthcare domain.$180,000 - $200,000 a year…