Applied AIML Data Scientist Lead - Vice President

Lead-level Applied AI/ML Data Scientist (Vice President) within JPMorgan Chase's Corporate Technology team, focusing on GenAI and LLM-driven solutions to address complex business problems and communicating insights to non-technical audiences. This onsite role is based in Jersey City, NJ, with a salary range of USD 164,350 to 260,000 per year, and requires a PhD or MS/BS in Computer Science or a related field along with relevant experience.

Responsibilities

• Collaborate with product managers, data scientists, ML engineers, and stakeholders to capture requirements and prioritize use cases.
• Design GenAI and LLM based solutions to address business challenges.
• Apply optimization strategies to tailor generative models for specific GenAI use cases to ensure high-quality outputs.
• Manage end-to-end model development workflow, including data wrangling and analysis, model training, testing, and selection.
• Generate structured, actionable insights from data analysis and modelling, presenting results in formats appropriate to the audience.
• Communicate AI and GenAI capabilities and results to both technical and non-technical audiences.
• Monitor evolving AI research trends, implement state-of-the-art techniques, and leverage external APIs to enhance functionality.

Requirements

• PhD in Computer Science or a related quantitative discipline with 2+ years of relevant experience, or MS/BS in Computer Science or a related field with 4+ years of relevant experience.
• Hands-on experience with LLM projects as well as other supervised and unsupervised techniques; a proven track record deploying AI/ML applications in production.
• Proficient in Python and SQL, with practical experience in additional languages such as R and Java.
• Demonstrated experience working with large and complex datasets.
• Experience with ML frameworks, libraries, and APIs such as TensorFlow, PyTorch, Scikit-learn, and the OpenAI API.
• Experience integrating user feedback to drive refinements and self-improving AI applications.
• Strong understanding of statistics and machine learning fundamentals, including classification, regression, time series, deep learning, reinforcement learning, and transformer-based generative models.
• Ability to identify AI and LLM challenges, implement optimizations, and tune models for optimal NLP performance.
• Excellent problem solving, verbal and written communication, and collaboration skills.

Technologies

• Python
• Spark
• AWS
• TensorFlow
• PyTorch
• Scikit-learn
• OpenAI API
• R
• Java

Benefits

• base salary
• commission-based pay
• discretionary incentive compensation (cash and/or forfeitable equity)
• comprehensive health care coverage
• on-site health and wellness centers
• retirement savings plan
• backup childcare
• tuition reimbursement
• mental health support
• financial coaching