{"id":3939774,"url":"https://alion.io/job/bmc-software-data-scientist-iii-2","title":"Data Scientist III","company":{"id":58475,"name":"BMC Software","domain":"bmc.com","url":"https://alion.io/company/bmc-software","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":{"grade":"A","score":99,"open_postings":10,"ghost_share":0,"stale_share":0,"repost_share":0.4,"time_to_fill_p50_days":15,"computed_at":"2026-11-08T05:47:12Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":131700,"max":219500,"currency":"USD","period":"year","gross":null,"usd_annual":219500},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Context Engineering","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"NumPy","optional":false},{"name":"OpenShift","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SFT","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorFlow","optional":false},{"name":"Agile","optional":true},{"name":"Confluence","optional":true},{"name":"CrewAI","optional":true},{"name":"DeepEval","optional":true},{"name":"ElasticSearch","optional":true},{"name":"FAISS","optional":true},{"name":"FastAPI","optional":true},{"name":"Flask","optional":true},{"name":"Google ADK","optional":true},{"name":"Jira","optional":true},{"name":"Kubeflow","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LangSmith","optional":true},{"name":"Milvus","optional":true},{"name":"MLFlow","optional":true},{"name":"OpenAI Agents SDK","optional":true},{"name":"OpenSearch","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"PEFT","optional":true},{"name":"pgvector","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Quantization","optional":true},{"name":"Ragas","optional":true},{"name":"Time Series","optional":true},{"name":"Triton","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-11-08T12:35:56Z","employer_posted_date":"2026-11-08","last_verified_at":"2026-11-08T12:35:56Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"CareerArc Code\nCA-MH\nHybrid: #LI-Hybrid\nBMC 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.\nWe'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.\nAbout the Role:\nYou 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.\nYou 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.\nKey Responsibilities:\nMachine Learning & Applied Data Science \nApply classification, regression, ranking, forecasting, clustering, and anomaly detection to enterprise AI and agentic use cases. \nDevelop and evaluate intent classification, semantic matching, agent/skill/tool ranking, and model-routing techniques using traditional ML, embeddings, and LLM-based approaches. \nApply statistical modeling to analyze execution latency, token usage, inference costs, workload trends, and resource requirements. \nUse clustering and anomaly detection to identify recurring intents, unusual agent behavior, capability gaps, and execution regressions. \nConduct exploratory analysis, dataset preparation, feature engineering, hypothesis testing, and reproducible experiments. \nFoundation Models, Prompting & Context Engineering \nEvaluate commercial and self-hosted open-weight models for accuracy, instruction adherence, structured outputs, latency, and cost. \nDevelop and test prompt templates, few-shot examples, tool-calling prompts, and context assembly approaches. \nCompare model alternatives for defined enterprise use cases and document quality, performance, and cost tradeoffs. \nCollaborate with Senior Data Scientists and AI Engineering on model performance analysis and inference optimization.\nRetrieval-Augmented Generation (RAG) \nDevelop and evaluate document chunking, metadata, embedding, retrieval, reranking, and context selection strategies. \nCompare vector, keyword/BM25, hybrid, and semantic retrieval approaches. \nMeasure retrieval effectiveness using Recall@K, Precision@K, nDCG, groundedness, faithfulness, and answer quality. \nInvestigate retrieval and generation failures and recommend improvements based on evaluation results. \nBenchmark embedding and reranking models using representative enterprise datasets.\nModel Fine-Tuning & Adaptation \nSupport evaluation of prompting, RAG, supervised instruction fine-tuning, and parameter-efficient model adaptation approaches. \nPrepare and validate instruction datasets, training examples, and holdout evaluation datasets. \nParticipate in SFT, LoRA, QLoRA, or comparable fine-tuning experiments under appropriate technical guidance. \nCompare adapted models against baseline models for accuracy, generalization, regressions, and operational cost. \nCollaborate on validation and production handoff of fine-tuned model artifacts. \nAgentic AI, MCP & A2A \nApply modeling and evaluation techniques to intent classification, agent/skill discovery, tool selection, and routing effectiveness. \nEvaluate task completion, tool-call correctness, parameter extraction, delegation quality, and recovery behavior. \nUnderstand agentic AI concepts, including planning, tool calling, MCP tools, A2A Agent Cards, skills, and task delegation. \nCollaborate with AI Engineering to investigate model-related issues affecting agentic workflows. \nContribute model-based decision signals and evaluation approaches without owning agent runtime, MCP server, or A2A protocol implementation. \nAI Evaluation & Experimentation \nDevelop evaluation datasets, golden test cases, holdout sets, and automated evaluation workflows. \nEvaluate ML models, foundation models, prompts, RAG pipelines, fine-tuned models, and agentic AI behaviors. \nApply statistical analysis, human evaluation, and LLM-as-judge approaches with appropriate calibration. \nTrack accuracy, relevance, groundedness, instruction following, task completion, latency, and cost. \nInvestigate evaluation failures and recommend improvements based on experimental evidence. \nSupport automated regression testing and production readiness validation. \nMLOps, LLMOps & Model Lifecycle \nApply experiment tracking and versioning across datasets, code, model configurations, prompts, retrieval settings, metrics, and artifacts. \nDevelop and maintain reproducible model training, benchmarking, and evaluation workflows. \nUse model registries, experiment tracking tools, and automated evaluation pipelines. \nContribute to model quality gates, regression testing, and release-readiness checks. \nAnalyze model performance, quality drift, routing accuracy, latency, and inference cost signals. \nCollaborate with engineering teams on model packaging, deployment handoffs, monitoring, and lifecycle management. \nMust-Have Skills & Experience:\n5-9 years of relevant Data Science, Machine Learning, or Applied AI experience. \nBachelor's degree in Machine Learning, Statistics, Data Science, Physics, Computer Science, or a related field, or equivalent education and industry experience. \nStrong Python proficiency with Pandas, NumPy, scikit-learn, and PyTorch or TensorFlow; SQL and practical data preparation skills. \nSolid understanding of statistical analysis, experimental design, hypothesis testing, ML validation, classification, regression, ranking, clustering, forecasting, and anomaly detection. \nPractical experience with LLM prompting, embeddings, model comparison, and RAG concepts, including chunking, retrieval, and reranking. \nFamiliarity with instruction for fine-tuning, parameter-efficient adaptation, or model customization techniques. \nExperience developing evaluation datasets, automated quality metrics, and reproducible ML/LLM experiments. \nHands-on experience with MLOps/LLMOps practices, including experiment tracking, dataset/model/prompt versioning, reproducible workflows, evaluation automation, and monitoring. \nWorking knowledge of agentic AI concepts, including agent planning, routing, tool calling, MCP, A2A, and multi-agent patterns. \nUnderstanding model latency, throughput, token usage, inference cost, and model performance tradeoffs. \nFamiliarity with AWS and/or Kubernetes or Red Hat OpenShift (OCP), and collaboration with engineering teams. \nClear communication, problem-solving, cross-functional collaboration, and ability to manage multiple priorities. \nDemonstrated ability to deliver moderately complex AI/ML initiatives, make sound technical decisions, and mentor junior Data Scientists. \nExperience contributing to reusable modeling, evaluation, or MLOps/LLMOps practices within project teams. \nNice-to-Have Skills:\nExperience with OpenSearch, Elasticsearch, FAISS, Milvus, pgvector, or similar retrieval platforms. \nExposure to LangGraph, OpenAI Agents SDK, Google ADK, CrewAI, or comparable agentic frameworks. \nFamiliarity with MLflow, Kubeflow, Ragas, DeepEval, OpenJudge, LangSmith, or similar MLOps/LLMOps and evaluation tools. \nExposure to instruction fine-tuning, SFT, LoRA, QLoRA, or domain-specific model adaptation. \nFamiliarity with vLLM, NVIDIA Triton, quantization, GPU memory considerations, or inference optimization. \nPython backend APIs (FastAPI, Flask, or similar); OpenTelemetry and AI observability. \nAI safety, prompt injection evaluation, privacy, responsible AI, and model governance. \nTime-series forecasting, causal inference, or anomaly detection in enterprise systems data. \nAgile practices and Atlassian Jira/Confluence. \nMaster's degree in a related field. \n#LI-Remote\nOur commitment to you!\nBMC’s culture is built around its people. We have 6000+ brilliant minds working together across the globe. You won’t be known just by your employee number, but for your true authentic self. BMC lets you be YOU!\nIf after reading the above, You’re unsure if you meet the qualifications of this role but are deeply excited about BMC and this team, we still encourage you to apply! We want to attract talents from diverse backgrounds and experience to ensure we face the world together with the best ideas!\nBMC is committed to equal opportunity employment regardless of race, age, sex, creed, color, religion, citizenship status, sexual orientation, gender, gender expression, gender identity, national origin, disability, marital status, pregnancy, disabled veteran or status as a protected veteran. If you need a reasonable accommodation for any part of the application and hiring process, visit the accommodation request page.\nBMC Software maintains a strict policy of not requesting any form of payment in exchange for employment opportunities, upholding a fair and ethical hiring process.\nThe annual base salary range represents the low and high end of the BMC salary range for this position. Actual salaries depend on a wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training, licensure, and certifications; and other business and organizational needs.\nThe range listed is just one component of BMC's employee compensation package. Other rewards may include a variable plan and country specific benefits.\nAt BMC, it is not typical for an individual to be hired at /near the top of the range. 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