Finance Decision Optimization - Data Scientist Lead
Not Disclosed•Full-TimeOn-site
location_on709, Cherrington Road, Westerville, Sharon, Franklin County, Ohio, 43081, United States
Join an intellectually diverse team of economists, statisticians, engineers, and analytics professionals within the Finance Decision Optimization group at JPMorgan Chase & Co. This team is dedicated to quantitative modeling within Community & Consumer Banking (CCB), driving data-driven solutions that shape critical financial decisions.
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also support small businesses, nonprofits, and cities by delivering solutions to meet all their financial needs.
As a Data Scientist Lead, you will architect and build foundational agentic workflows from the ground up. This role sits at the intersection of advanced AI engineering and financial decision-making, focusing on tool/function calling, multi-step reasoning chains, and agent orchestration patterns. You will define success metrics for agent performance, build evaluation harnesses, and design retrieval layers (RAG) that allow agents to take actions safely and effectively.
Your day-to-day involves collaborating with cross-functional teams to define data and model requirements, ensuring solutions are robust, reliable, and decision-ready. You will lead solution backtesting exercises across key domains like Fair Lending, validate model performance against historical data, and proactively surface critical issues to business and technology partners. Beyond technical execution, you will serve as a mentor and knowledge resource for junior staff, establishing best practices in data engineering and fostering a culture of continuous learning and shared ownership.
Candidates selected for this role will engage in a structured interview process designed to assess technical depth, problem-solving abilities, and cultural fit. While specific steps may vary, the process typically includes an initial screening, technical deep-dives into agentic AI and data engineering, and discussions on system design and team collaboration.
We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy, or disability. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
Work model: On-site
709, Cherrington Road, Westerville, Sharon, Franklin County, Ohio, 43081, United States
Westerville, Ohio
Proficiency in Python programming with a strong grasp of object-oriented and functional programming concepts; experience applying Python in data processing, ML model development, and AI/LLM application development including prompt engineering and agentic workflow orchestration and hands-on experience with LLM orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex, or similar); familiarity with embedding models, vector databases (e.g., FAISS, Pinecone, pgvector), retrieval-augmented generation (RAG) pipelines, and evaluation frameworks for agentic systems. Extensive knowledge of Apache Spark with experience optimizing Spark jobs for performance and scalability within Databricks; hands-on experience with cloud platforms (AWS EC2, EMR, S3/EFS or equivalent) and proficiency with Snowflake for large-scale data processing and analytics. Advanced SQL skills for complex query writing, data manipulation, and analysis; strong experience in data engineering including ETL/ELT processes, data modeling, data governance, and compliance standards relevant to handling sensitive and regulated data and proficiency with the Python data science ecosystem (Pandas, NumPy, SciPy) and practical experience implementing and validating machine learning algorithms (e.g., XGBoost, TensorFlow) and ability to perform data analysis, cleansing, modeling (including time series and NLP), and visualization using tools such as Tableau or Alteryx to develop and automate actionable business insights. Expertise in Linux bash shell environment and Git for version control and collaborative development; familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes) to support scalable deployment of data and AI services and familiarity with implementing guardrails, input/output validation, human-in-the-loop checkpoints, and monitoring/observability patterns (action traces, decision logs, cost and latency tracking) for AI/agentic systems operating in regulated environments.
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Skills: Spark, Databricks, Snowflake, Aws, Ec2, Emr, S3, Efs, Python, LLM.
Education: Bachelor's degree in Computer Science, Financial Engineering, MIS, Mathematics, Statistics, or another quantitative field required.