Mid Level Associate Data Scientist Jobs

• 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.
• Collaborate on validation and production handoff of fine-tuned model artifacts.

• Apply modeling and evaluation techniques to intent classification, agent/skill discovery, tool selection, and routing effectiveness.
• Evaluate task completion, tool-call correctness, parameter extraction, delegation quality, and recovery behavior.
• Understand agentic AI concepts, including planning, tool calling, MCP tools, A2A Agent Cards, skills, and task delegation.
• Collaborate with AI Engineering to investigate model-related issues affecting agentic workflows.
• Contribute model-based decision signals and evaluation approaches without owning agent runtime, MCP server, or A2A protocol implementation.

• Develop evaluation datasets, golden test cases, holdout sets, and automated evaluation workflows.
• Evaluate ML models, foundation models, prompts, RAG pipelines, fine-tuned models, and agentic AI behaviors.
• Apply statistical analysis, human evaluation, and LLM-as-judge approaches with appropriate calibration.
• Track accuracy, relevance, groundedness, instruction following, task completion, latency, and cost.
• Investigate evaluation failures and recommend improvements based on experimental evidence.
• Support automated regression testing and production readiness validation.

• Apply experiment tracking and versioning across datasets, code, model configurations, prompts, retrieval settings, metrics, and artifacts.
• Develop and maintain reproducible model training, benchmarking, and evaluation workflows.
• Use model registries, experiment tracking tools, and automated evaluation pipelines.
• Contribute to model quality gates, regression testing, and release-readiness checks.
• Analyze model performance, quality drift, routing accuracy, latency, and inference cost signals.
• Collaborate with engineering teams on model packaging, deployment handoffs, monitoring, and lifecycle management.

• 5-9 years of relevant Data Science, Machine Learning, or Applied AI experience.
• Bachelor's degree in Machine Learning, Statistics, Data Science, Physics, Computer Science, or a related field, or equivalent education and industry experience.
• Strong Python proficiency with Pandas, NumPy, scikit-learn, and PyTorch or TensorFlow; SQL and practical data preparation skills.
• Solid understanding of statistical analysis, experimental design, hypothesis testing, ML validation, classification, regression, ranking, clustering, forecasting, and anomaly detection.
• Practical experience with LLM prompting, embeddings, model comparison, and RAG concepts, including chunking, retrieval, and reranking.
• Familiarity with instruction for fine-tuning, parameter-efficient adaptation, or model customization techniques.
• Experience developing evaluation datasets, automated quality metrics, and reproducible ML/LLM experiments.
• Hands-on experience with MLOps/LLMOps practices, including experiment tracking, dataset/model/prompt versioning, reproducible workflows, evaluation automation, and monitoring.
• Working knowledge of agentic AI concepts, including agent planning, routing, tool calling, MCP, A2A, and multi-agent patterns.
• Understanding model latency, throughput, token usage, inference cost, and model performance tradeoffs.
• Familiarity with AWS and/or Kubernetes or Red Hat OpenShift (OCP), and collaboration with engineering teams.
• Clear communication, problem-solving, cross-functional collaboration, and ability to manage multiple priorities.
• Demonstrated ability to deliver moderately complex AI/ML initiatives, make sound technical decisions, and mentor junior Data Scientists.
• Experience contributing to reusable modeling, evaluation, or MLOps/LLMOps practices within project teams.

• Experience with OpenSearch, Elasticsearch, FAISS, Milvus, pgvector, or similar retrieval platforms.
• Exposure to LangGraph, OpenAI Agents SDK, Google ADK, CrewAI, or comparable agentic frameworks.
• Familiarity with MLflow, Kubeflow, Ragas, DeepEval, OpenJudge, LangSmith, or similar MLOps/LLMOps and evaluation tools.
• Exposure to instruction fine-tuning, SFT, LoRA, QLoRA, or domain-specific model adaptation.
• Familiarity with vLLM, NVIDIA Triton, quantization, GPU memory considerations, or inference optimization.
• Python backend APIs (FastAPI, Flask, or similar); OpenTelemetry and AI observability.
• AI safety, prompt injection evaluation, privacy, responsible AI, and model governance.
• Time-series forecasting, causal inference, or anomaly detection in enterprise systems data.
• Agile practices and Atlassian Jira/Confluence.
• Master's degree in a related field.

Our commitment to you!

BMC’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!

If 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!

BMC 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.