Senior Data Scientist

You may occasionally be required to travel for business

This role can be based remotely in United Kingdom

BMC empowers nearly 80% of the Forbes Global 100 to accelerate business value, faster than humanly possible. Our industry-leading portfolio unlocks human and machine potential to drive business growth, innovation, and sustainable success. BMC does this in a simple and optimized way by connecting people, systems, and data that power the world’s largest organizations so they can seize a competitive advantage.

We're looking for a Senior Data Scientist to join our AMI group. This hands‑on role combines Machine Learning, statistical modeling, Generative AI, RAG, model evaluation, and fine‑tuning for enterprise AI applications. AMI supports commercial AI providers and on‑premises models, with agents, MCP tools, and A2A interoperability delivered in partnership with AI Engineering.

About the Role:

You will independently design, develop, evaluate, and operationalize complex AI/ML solutions. You will contribute hands‑on technical expertise, apply rigorous experimentation and MLOps/LLMOps practices, improve modeling and evaluation approaches within assigned initiatives, and mentor other Data Scientists.

You will be a part of the AI Migration team
, responsible for building tools that help customers transition from competitor products to BMC solutions. You'll work at the intersection of software engineering and AI, combining code‑based conversion with AI‑assisted processing to tackle migration challenges. Your work will help reduce manual effort and enable services to deliver accurate, timely customer migrations

Key Responsibilities:

• Apply classification, regression, ranking, forecasting, clustering, and anomaly detection to enterprise AI and agentic use cases.
• Develop and evaluate intent classification, semantic matching, agent/skill/tool ranking, and model‑routing techniques; compare learned approaches with rules, embeddings, and LLM‑based baselines.
• Use statistical modeling to estimate execution latency, token demand, inference costs, workload trends, and resource requirements.
• Apply clustering and anomaly detection to identify recurring intents, capability gaps, unusual agent behavior, and execution regressions.
• Conduct exploratory analysis, dataset preparation, feature engineering, hypothesis testing, and reproducible experiments.

• Evaluate commercial API and self‑hosted open‑weight models for accuracy, reasoning, instruction adherence, structured outputs, latency, throughput, and total cost.
• Design and optimize system instructions, prompt templates, few‑shot examples, tool‑calling prompts, and context assembly strategies.
• Benchmark model alternatives for specific enterprise workloads and document quality/cost/latency tradeoffs.
• Collaborate with AI Engineering and Platform Engineering on inference performance, quantization tradeoffs, and GPU/VRAM capacity assumptions.

• Evaluate commercial API and self‑hosted open‑weight models for accuracy, reasoning, instruction adherence, structured outputs, latency, throughput, and total cost.
• Design and optimize system instructions, prompt templates, few‑shot examples, tool‑calling prompts, and context assembly strategies.
• Benchmark model alternatives for specific enterprise workloads and document quality/cost/latency tradeoffs.
• Collaborate with AI Engineering and Platform Engineering on inference performance, quantization tradeoffs, and GPU/VRAM capacity assumptions.

• Design and optimize document parsing, chunking, metadata, embedding, retrieval, reranking, and context selection strategies.
• Evaluate vector, keyword/BM25, hybrid, and semantic retrieval,…