The Risk Management & Compliance Technology Machine Learning team at JPMorgan Chase focuses on solving challenging business problems such as Anti-Money Laundering and Surveillance through data science and machine learning techniques across Risk, Compliance, Conduct and Operational Risk. As an Applied AI ML Lead on the team, you will have the opportunity to study complex business problems, propose appropriate solutions, and apply your expertise in advanced AI/ML algorithms to develop, test, and evaluate solutions for those problems. 

You will work with the firm’s rich data pool from both internal and external sources using Python/Spark via AWS and other systems. You are expected to own the lifecycle of a defined project and work with other team members to drive delivery of impactful results. You are also expected to derive business insights from technical results and significantly contribute to discussions with our business stakeholders and partners. As a senior member on the team, you will also serve as a mentor to junior data scientists to foster a collaborative working environment. 

Job responsibilities

Actively develop thorough understanding of complex business problems and processes; discover opportunities for AI and ML solutions.  Collaborate with business partners to drive data-led transformations of the businesses.  Own machine learning development lifecycle activities and execute on crucial timelines and milestones.  Lead tasks throughout a model development process including data wrangling/analysis, model training, testing, and selection.  Generate structured and meaningful insights from data analysis and modelling exercise and present them in appropriate format according to the audience.  Provide mentorship and oversight for junior data scientists to build a collaborative working culture.  Partner with machine learning engineers to deploy machine learning solutions.  Own key model maintenance tasks and lead remediation actions as needed.  Stay informed about the latest trends in the AI/ML/LLM/GenAI research and operate with a continuous-improvement mindset.  

Required qualifications, capabilities, and skills

Advanced degree (MS, PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics). At least 5 years of relevant experience in applied AI/ML domain.   In-depth expertise and extensive experience with ML projects, both supervised and unsupervised.   Strong programming skills with Python, R, or other equivalent languages.  Proficient in working with large datasets and handling complex data issues.  Experience with broad range of analytical toolkits, such as SQL, Spark, Scikit-Learn, XGBoost, graph analytics, and neural nets.  Excellent solution ideation, problem solving, communication (verbal and written), and teamwork skills.  

Preferred qualifications, capabilities, and skills

Familiarity with machine learning engineering and developing/implementing machine learning models within AWS or other cloud platforms.  Familiarity with the financial services industry.