Applied AIML Data Scientist Lead-Vice President

J.P. Morgan; Plano (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

An established industry player is seeking an Applied AI ML Lead to tackle complex business challenges through innovative machine learning solutions. This role offers the opportunity to work with vast datasets and cutting-edge technologies, driving impactful results while mentoring junior data scientists. You will engage with business partners to transform operations with data-led insights, utilizing your expertise in AI/ML algorithms to develop, test, and evaluate solutions. Join a dynamic team that values collaboration and continuous improvement in a fast-paced environment, where your contributions will significantly influence the company's success.

Qualifications

• 5+ years of experience in applied AI/ML with a focus on complex business problems.
• Advanced degree in a quantitative field such as Data Science or Computer Science.

Responsibilities

• Develop a thorough understanding of complex business problems and propose AI/ML solutions.
• Lead model development tasks including data analysis, model training, and testing.

Skills

Machine Learning

Data Science

Python

R

SQL

Spark

Scikit-Learn

XGBoost

Statistical Analysis

Problem Solving

Education

Master's Degree in Data Science

PhD in Computer Science

Tools

AWS

Spark

Scikit-Learn

XGBoost

Job description

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.