What are the responsibilities and job description for the Lead Data Scientist - Finance Technology position at JPMorgan Chase?
Join our Finance Technology team to build world-class AI/ML solutions for finance processes and drive significant business impact by tackling diverse challenges across multiple technologies and applications.
As a Lead Data Scientist in our Finance Technology team, you will play a pivotal role in building and delivering cutting-edge AI/ML solutions that enhance our finance processes. Your deep technical expertise and problem-solving skills will drive significant business impact, as you work on diverse challenges across multiple technologies and applications. Collaborate with business users and technology partners to identify and execute machine learning opportunities, while managing a global team of data scientists and engineers.
Job Responsibilities:
- Build and train production-grade ML models on large-scale datasets to solve business use cases.
- Utilize large-scale data processing frameworks to extract value from structured and unstructured data.
- Apply Deep Learning models like NLP, LLM, and Gen AI for summarization, forecasting, and anomaly detection.
- Conduct data modeling experiments, evaluate against baselines, and extract key statistical insights.
- Create data models using best practices to ensure high data quality and reduced redundancy.
- Stay current on industry trends and adopt the latest methodologies into existing implementations.
- Present and market proposed solutions to senior business and technology colleagues.
- Collaborate closely with business users to identify and execute machine learning opportunities.
- Work with the team and other technology partners on ML Ops aspects.
- Manage a global team of data scientists and data engineers.
Required Qualifications, Capabilities, and Skills:
- Master's Degree or equivalent experience in Computer Science or Data Science.
- 12 years of experience as a data scientist.
- Experience with machine learning techniques and advanced analytics (e.g., regression, classification, clustering, time series, econometrics, causal inference, mathematical optimization, NLP).
- Experience with LLM and Gen AI.
- Experience with Agentic workflows.
- Proficiency in ML languages such as Python, SQL, Scala.
- Experience with statistical techniques - i.e., data mining, data transformations, text mining, data visualization.
Preferred Qualifications, Capabilities, and Skills:
- Familiarity with Financial Services.
- Experience building ML models in a cloud environment.
- Experience with Big Data Platforms such as Hadoop.
- Outstanding written/verbal communication and presentation skills.
- Comfort with ambiguity and proven ability to structure problems.
- Team-oriented collaborator.