Senior Associate -Applied AI Data Scientist

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Category:
Quant Analytics

Job Schedule:

Full time

Posted Date: T19:35:49+00:00

Job Shift: Day

Base Pay/Salary:
Jersey City,NJ $-$

About the role JPMorgan Chase's Asset & Wealth Management Finance organization is building the next generation of agentic AI solutions that act as "digital workers" for forecasting, analytics, and decision support.

As a Senior Data Science Associate, you will design, deploy, and scale large language model (LLM) agents that turn complex finance questions into trusted, actionable insights.

Job responsibilities

* Build production LLM agents for finance workflows using techniques such as retrieval‑augmented generation (RAG), tool use, and multi‑step reasoning.

* Develop robust data and inference pipelines in Python and SQL; integrate agents with APIs, microservices, and BI applications.

* Implement evaluation frameworks and guardrails: offline and online tests, automatic metrics (factuality, grounding, hallucination rate), human‑in‑the‑loop reviews, red‑team testing, and observability.

* Optimize for scale, latency, and cost across cloud environments; leverage vector databases and embeddings for efficient retrieval.

* Partner with Finance, Product, and Engineering to identify high‑value use cases; translate ambiguous problems into measurable outcomes.

* Apply solid ML engineering and MLOps practices (versioning, CI/CD, model registry, monitoring, incident response).

* Document systems, deliver enablement materials, and upskill partners; contribute to standards for privacy, security, and model risk governance.

Required qualifications, capabilities and skills

* 6+ years in data/ML roles, including 3+ years building and operating production ML applications; hands‑on experience with LLMs.

* Strong Python and SQL.

* Practical knowledge of RAG, prompt engineering, fine‑tuning, function/tool calling, and vector stores.

* Experience with cloud platforms (e.g., AWS, Azure, or GCP) and modern data stacks (e.g., Databricks or Snowflake).

* Familiarity with LLM frameworks and orchestration (e.g., Lang Chain or Llama Index) and REST/Graph

QL API design.

* Proficiency in analytics and applied statistics; ability to design experiments and evaluate business impact.

* Excellent communication and stakeholder management; comfort working across Finance, Technology, and Operations.

Preferred qualifications, capabilities and skills

* Experience building multi‑agent systems, autonomous workflows, or task planners.

* Eexperience with PySpark or distributed compute.

* Knowledge of model safety, bias, and privacy techniques; experience with model risk management and governance.

* Exposure to observability tools (logging, tracing, telemetry) and A/B testing.

* Background integrating agents with BI/reporting and workflow tools; familiarity with Tableau or similar is a plus.

* Experience with GPUs/accelerators, containerization, and infrastructure‑as‑code.

What success looks like

* 90 days: deliver a pilot finance agent with RAG and evaluation metrics, integrated with key data sources and APIs.

* 6 months: scale agents across multiple workflows, establish guardrails and monitoring, and demonstrate clear improvements in cycle time, accuracy, or user satisfaction.