Applied AI ML Lead
About The Position
Join JPMorgan Chase and help shape the future of AI-driven innovation in financial services. As Lead Applied AI ML - Data Scientist within the Commercial & Investment Bank's team , you’ll leverage your technical expertise and leadership abilities to support AI innovation. You should have deep knowledge of AI/ML and effective leadership to inspire the team, align cross-functional stakeholders, engage senior leadership, and drive business results. Job Responsibilities Lead a local applied machine learning team and collaborate across a global organization to deliver high‑impact outcomes. Define technical vision, shape strategic roadmaps, and align stakeholders across product, business, and technology. Translate business requirements into machine learning specifications, milestones, and agile delivery plans. Design experiments, implement algorithms, validate results, and productionize scalable, trustworthy, and explainable solutions. Refine model capabilities using PyTorch and scikit‑learn; apply causal inference with DoWhy to quantify treatment effects and inform experimental decisions. Utilize Hugging Face Transformers and LangChain to explore counterfactual reasoning in large language models; produce research materials, internal notes, and demos that enable stakeholder adoption. Build and operate model development and operations workflows for training, deployment, monitoring, and continuous improvement. Exercise sound technical judgment, anticipate bottlenecks, and balance business needs with technical constraints. Mentor and coach team members, foster an inclusive culture, and grow talent. Contribute to firmwide machine learning communities through publications, talks, patents, and knowledge sharing. Evaluate and improve processes that enhance execution, communication, and accountability.
Requirements
- Masters with 7+ years experience or PhD with 3+ years of experience in Computer Science, Information Systems, Statistics, Mathematics, or equivalent experience.
- Track record of managing AI/ML or software development teams.
- Experience as a hands-on practitioner developing production AI/ML solutions.
- Knowledge and experience in machine learning and artificial intelligence.
- Ability to set teams up for success in speed and quality, and design effective metrics and hypotheses.
- Expert in at least one of the following areas: Large Language Models, Natural Language Processing, Knowledge Graph, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis.
- Good understanding of Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics.
- Must have good knowledge on agentic patterns and relevant frameworks, such as LangChain, LangGraph, Auto-GPT etc.
- Strong understanding of AI implementation in software development and legacy code transformation.
- Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, AI evaluation, RAG (Similarity Search).
- Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn.
- Programming knowledge of python, spark; Strong grasp on vector operations using numpy, scipy etc
Nice To Haves
- Familiarity in AWS Cloud services such as EMR, Sagemaker etc.
- Strong people management and team-building skills.
- Ability to coach and grow talent, foster a healthy engineering culture, and attract/retain talent.
- Ability to build a diverse, inclusive, and high-performing team.
- Ability to inspire collaboration among teams composed of both technical and non-technical members.
- Effective communication, solid negotiation skills, and strong leadership.
Responsibilities
- Lead a local applied machine learning team and collaborate across a global organization to deliver high‑impact outcomes.
- Define technical vision, shape strategic roadmaps, and align stakeholders across product, business, and technology.
- Translate business requirements into machine learning specifications, milestones, and agile delivery plans.
- Design experiments, implement algorithms, validate results, and productionize scalable, trustworthy, and explainable solutions.
- Refine model capabilities using PyTorch and scikit‑learn; apply causal inference with DoWhy to quantify treatment effects and inform experimental decisions.
- Utilize Hugging Face Transformers and LangChain to explore counterfactual reasoning in large language models; produce research materials, internal notes, and demos that enable stakeholder adoption.
- Build and operate model development and operations workflows for training, deployment, monitoring, and continuous improvement.
- Exercise sound technical judgment, anticipate bottlenecks, and balance business needs with technical constraints.
- Mentor and coach team members, foster an inclusive culture, and grow talent.
- Contribute to firmwide machine learning communities through publications, talks, patents, and knowledge sharing.
- Evaluate and improve processes that enhance execution, communication, and accountability.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
