Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.

 As a Lead Data Scientist Engineer at JPMorgan Chase within the Corporate Data Services team, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

 

Job Responsibilities:

Leading the development and delivery of data science solutions to unlock the power of our firmwide data Working with peers and stakeholders to identify use cases and opportunities that will create value Designing and delivering solutions that are flexible and scalable using the firm’s approved tools Developing cutting edge solutions for critical functions such as Anomaly Detection and LLM on big data. Communicating analytical findings to senior leaders through data visualization and storytelling Deriving and delivering key insights and analytics on a broad scale Understanding multiple data sources with both structured and unstructured files and how to classify and aggregate and document for repeatable usage

 

Required qualifications, capabilities, and skills:

5+ years’ experience as a Data Scientist or in an adjacent quantitative role 5+ years' experience in a corporate environment Databricks ML Flow, Sagemaker Studio, Juptyr Labs Expert knowledge of using Python for implementing feature engineering, model training and evaluation. Knowledge of a range of languages and tools (e.g., SQL, Python, Databricks, AWS Sagemaker) and data visualization solutions Proven expertise in handling complex high dimensional Anomaly Detection models Experience in LLM implementations, ML solutions such as insights, pattern detection, generative AI, forecasting, and ML algorithms

 

Preferred qualifications, capabilities, and skills

Experience working at code level  Masters/PhD in a quantitative or related discipline  Experience in the financial domain or organization