Provide tailored guidance to business units on AI/ML use cases, feasibility, model selection, and deployment options, particularly in scientific domains without active AI/ML engineering efforts.
Co-design prototypes and proof-of-concepts (PoCs) with product and domain teams to validate ideas quickly and de-risk larger investments.
Translate complex stakeholder requirements into well-scoped technical solutions with clear success criteria and handover plans.
Build, train, evaluate, and iterate on ML models for real-world scientific and business problems—including but not limited to NLP/LLM applications, knowledge graphs, causal inference, computer vision, and predictive modeling.
Package trained models into production-ready services (APIs, containerized deployments) using GSK’s cloud infrastructure (GCP/AWS/Azure).
Develop and maintain agentic AI systems, multi-agent architectures, and LLM-based tools where appropriate.
Share reusable patterns, baseline models, and tested pipelines for common AI/ML tasks.
Embed privacy, ethics, and regulatory considerations into every engagement from the outset.
Run workshops, seminars, and hands-on training sessions to increase AI literacy across the organization.
Embed within business/research units for time-limited engagements (typically 6–8 weeks) to accelerate delivery and transfer skills.
Communicate relevant issues, requests, and opportunities from business units back to AI/ML product leads.

Requirements

Bachelor’s degree in computer science, Machine Learning, Computational Biology, Bioinformatics, Statistics, Engineering, or a related quantitative discipline; OR equivalent professional experience as a software/ML engineer.
2+ years of professional experience developing and deploying machine learning models (with a Bachelor’s); 1+ years with a Master’s or PhD.
Expertise in Python, including ML/data science libraries (PyTorch, TensorFlow, JAX, scikit-learn, pandas, numpy).
Experience with cloud platforms (GCP, AWS, or Azure) and containerization (Docker, Kubernetes).
Strong understanding of ML fundamentals: supervised/unsupervised learning, deep learning, model evaluation, feature engineering, and experiment tracking.
Experience working in healthcare, pharma, or biological domains.

Benefits

health care and other insurance benefits (for employee and family)
retirement benefits
paid holidays
vacation
paid caregiver/parental and medical leave

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningnatural language processinglarge language modelsknowledge graphscausal inferencecomputer visionpredictive modelingmodel evaluationfeature engineeringexperiment tracking

Soft Skills

communicationstakeholder managementtrainingcollaborationproblem-solvingguidanceworkshop facilitationtechnical translationorganizational skillsadaptability