{"id":2745155,"url":"https://alion.io/job/jpmorganchase-data-scientist-associate-10","title":"Data Scientist - Associate","company":{"id":257,"name":"JPMorganChase","domain":"jpmorganchase.com","url":"https://alion.io/company/jpmorganchase","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":535,"ghost_share":0.011,"stale_share":0.123,"repost_share":0.114,"time_to_fill_p50_days":8,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":60000,"period":"year","method":"role_country_seniority_unknown","sample_n":141},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"Arize Phoenix","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LangChain","optional":true},{"name":"LangSmith","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Multimodal AI","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Synthetic Data","optional":true}],"status":"live","first_seen_at":"2026-10-13T09:21:22Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-18T21:56:13Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"You are a strategic thinker passionate about driving solutions in “Applied AI & Machine Learning”. You have found the right team.\nAs “Applied AI & Machine Learning Professional” within our “Asset Management Data Science Team ” , you will be responsible for setting and improving our organizational objectives, and ensuring their consistent accomplishment.\nJob Responsibilities\nDesign and implement scalable architecture for LLM-powered investment tools, including reusable patterns for integration, governance, observability, and access control\nBuild and scale AI applications and automated workflows using models, retrieval, and tools with orchestration for state management, retries, checkpointing, deterministic controls, and appropriate human oversight\nDevelop context-management and retrieval-augmented generation (RAG) capabilities, including ingestion, chunking, metadata, embeddings, hybrid search, reranking, and grounded outputs\nCreate reliable data and knowledge pipelines that transform structured and unstructured enterprise content into governed, high-quality inputs for AI applications\nEstablish engineering standards for reusable tools and model integrations, including interfaces, permissions, testing, failure handling, and documentation\nImplement evaluation and end-to-end observability for AI systems, including structured logging and tracing across model calls, retrieval steps, and tool execution\nOptimize AI systems for quality, task completion, latency, token usage, cost, reliability, and business impact\nPartner with portfolio managers and research teams to understand investment processes and translate them into practical solutions within the firm’s ecosystem\nRequired qualifications, capabilities and skills\nEarn a Master’s degree or PhD in computer science, statistics, mathematics, engineering, econometrics, or another quantitative field\nApply a strong foundation in statistics, probability, experimental design, and machine learning to select appropriate methods and interpret quantitative results\nBuild, deploy, and scale LLM-powered 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performance attribution, or decision analytics\nIncorporate unstructured or alternative data into research and production workflows\nApply familiarity with Model Context Protocol (MCP) or comparable standards for securely connecting AI applications to enterprise data, tools, and services\nUse LLM observability and evaluation standards or platforms such as OpenTelemetry, OpenInference, Arize Phoenix, LangSmith, or equivalent (including traces across model, retrieval, and tool-execution steps)\nLeverage familiarity with knowledge graphs, multimodal models, fine-tuning, synthetic data, or advanced model-evaluation techniques\nPursue CFA qualification or progress toward the CFA 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