Machine Learning / Data Analytics - Lead Software Engineer

We have an opportunity to impact your career and provide an adventure where you can push the limits of advanced analytics.

As a Data Analytics Lead Software Engineer at JPMorgan Chase within the Corporate Technology team, you are an integral part of an agile team that works to enhance, build, and deliver trusted data technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical machine learning solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Effectively collaborate with business stakeholders and Data Scientists to dive deep into the data and extract key insights
• Identify and implement best practices in machine learning to interpret data with special focus on anomaly detection
• Proven hands on experience creating and implementing machine learning applications on the Cloud (Databricks/AWS)
• Show understanding of data relationships (Logical and Physical models)
• Create an end to end system architecture for a Data Analytics application
• Develops secure high-quality production code, and reviews and debugs code written by others
• Manage building system designs for data pipelines geared towards analytics
• Manage business stakeholder, understanding key business requirements and creating system design would be required

Required qualifications, capabilities, and skills

• Formal training or certification on data engineering and machine learning concepts and 5 years of applied experience
• Prior experience working with Databricks Lakehouse architecture dealing with high volume relational data 
• Hands-on practical experience delivering system design, application development, testing, and operational stability
• Demonstrate ability to create Data Pipelines using Python libraries such as PySpark, Pandas, NumPy
• Experience with Modern Analytics tools such as Looker, Thoughtspot, Tableau, Quik Sense
• Proficiency in automation and continuous delivery methods
• Proficient in all aspects of the Software Development Life Cycle
• Advanced understanding of agile methodologies 
• In-depth knowledge of the financial services industry and their IT systems
• Practical AWS cloud native experience

Preferred qualifications, capabilities, and skills

• Prior experience working with Anomaly Detection models
• Extensive experience working with Data Science teams to develop, train, test and deploy AI/ML applications
• Experience in AI particularly working with Large Language Models