Lead Software Engineer - Lead Data Scientist/Engineer

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Consumer and Community Banking in our Architecture and Engineering team,  you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

We are looking for an experienced Data Scientist/Engineer  to join our AI Services team of talented technologists. The successful candidate will have a passion for data, ML, Software Development with an emphasis on understanding the data landscape in large and complex organizations

Job responsibilities

• Collaborate with all of JPMorgan’s lines of business and functions to delivery software solutions.
• Experiment, develop and productionize high quality machine learning models, services and platforms to make huge technology and business impact.
• Design and implement highly scalable and reliable data processing pipelines and perform analysis and insights to drive and optimize business result.

Required qualifications, capabilities, and skills

• Formal training or certification on Data Scientist/Engineer and 5 years applied experience
• Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis, Operations Research).
• Proven Software Engineering Expertise, building, maintaining and deploying enterprise grade solutions with resiliency and fault tolerance using modern engineering principles and practices. 
• Strong hands-on experience of advanced data mining techniques, curating, processing and transforming data to produce sound datasets.
• Strong hands-on experience of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop.
• Experience in analyzing complex problems and translating it into an analytical approach.
• Experience in Supervised and Unsupervised Machine Learning including Classification, Forecasting, Anomaly Detection, Pattern Detection, Text Mining, using variety of techniques such as Decision trees, Time Series Analysis, Bagging and Boosting algorithms, Neural Networks, LLMs
• Experience with analytical programming languages, tools and libraries (Python ecosystem preferred).
• Good grasp and knowledge around using Numpy, Matplotlib, Pandas and Seaborn libraries for everyday statistical tasks, hypothesis validation and any presentation required
• Experience in SQL and relational databases, Big Data technologies e.g. Spark/Hadoop and Cloud technologies.
• Strong leadership, stakeholder management, communication, partnership and teamwork skills.