Data Scientist III

"At BMC trust is not just a word - it's a way of life!"

Career Arc Code

CA-MH

Hybrid

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 an experienced Data Scientist to join our AMI team. This hands‑on role combines Machine Learning, statistical modeling, Generative AI, Retrieval‑Augmented Generation (RAG), model evaluation, and applied AI experimentation 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.

You will design, develop, evaluate, and improve AI/ML solutions that address enterprise business and technical challenges. You will apply statistical rigor, reproducible experimentation, and MLOps/LLMOps practices to improve model quality, RAG effectiveness, and AI application performance.

You will take ownership of defined Data Science initiatives, lead moderately complex or ambiguous modeling and evaluation tasks, collaborate with engineering teams on production readiness, and mentor less experienced Data Scientists.

• 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 using traditional ML, embeddings, and LLM‑based approaches.
• Apply statistical modeling to analyze execution latency, token usage, inference costs, workload trends, and resource requirements.
• Use clustering and anomaly detection to identify recurring intents, unusual agent behavior, capability gaps, and execution regressions.
• Conduct exploratory analysis, dataset preparation, feature engineering, hypothesis testing, and reproducible experiments.

• Evaluate commercial and self‑hosted open‑weight models for accuracy, instruction adherence, structured outputs, latency, and cost.
• Develop and test prompt templates, few‑shot examples, tool‑calling prompts, and context assembly approaches.
• Compare model alternatives for defined enterprise use cases and document quality, performance, and cost tradeoffs.
• Collaborate with Senior Data Scientists and AI Engineering on model performance analysis and inference optimization.

• Develop and evaluate document chunking, metadata, embedding, retrieval, reranking, and context selection strategies.
• Compare vector, keyword/BM25, hybrid, and semantic retrieval approaches.
• Measure retrieval effectiveness using Recall@K, Precision@K, nDCG, groundedness, faithfulness, and answer quality.
• Investigate retrieval and generation failures and recommend improvements based on evaluation results.
• Benchmark embedding and reranking models using representative enterprise datasets.

• Support evaluation of prompting, RAG, supervised instruction fine‑tuning, and parameter‑efficient model adaptation approaches.
• Prepare and validate instruction datasets, training examples, and holdout evaluation datasets.
• Participate in SFT, LoRA, QLoRA, or comparable fine‑tuning experiments under appropriate technical guidance.
• Compare adapted models against baseline models for accuracy, generalization, regressions, and operational cost.
• Collab…