Lead Data Scientist -Platform AI Acceleration

Overview

In this role you lead the design, build, and operation of production-grade ML and Generative AI services within the Applied AI/ML team. You will steer technical direction across workstreams, stay close to code and architecture, and ensure secure, reliable delivery at enterprise scale. You’ll mentor engineers, define standards, and drive AI adoption across Infrastructure Platforms to address priority use cases. This is a hands-on leadership position shaping AI engineering at scale in a secure, enterprise environment.

Responsibilities

• Analyze large datasets to extract insights and inform decisions
• Evaluate and harden AI use cases on enterprise platforms with automated data profiling and quality checks
• End-to-end model development across ML, deep learning, and LLM-based approaches including training, tuning, calibration, and failure-mode analysis
• Co-develop and implement LLM-based models to solve complex operational challenges
• Ship reusable assets (playbooks, templates, reference implementations) and improve them from production telemetry and incidents
• Collaborate with wider technology groups to translate business needs into technical solutions
• Define standards to ensure regulatory and data-privacy considerations are embedded in system design

Key requirements

• Post Graduate qualification (Masters or PhD) in Data Science, Computer Science, or Mathematics
• Hands-on production ML/AI system development across statistics, ML, deep learning, and LLMs
• Strong grounding in statistics, probability, and experimental design
• Deep experience with PyTorch and/or TensorFlow, scikit-learn, Hugging Face Transformers
• Distributed training and scalable model serving experience
• Experience deploying/operating models in cloud production environments (training/tuning workflows, inference, monitoring)
• Strong knowledge of LLMs/SLMs, including fine-tuning, deployment, and cost/latency considerations
• Hands-on design/operation of RAG systems with grounding controls
• Experience with agentic AI, tool calling, orchestration patterns, guardrails, and safe outputs
• leadership and mentoring
• cross-functional collaboration
• strong communication
• PyTorch
• TensorFlow
• scikit-learn

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