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 Wealth Management - Applied AI & Analytics team, you play a crucial role in an agile team dedicated to enhancing, building, and delivering market-leading technology products that are secure, stable, and scalable. You are a key technical contributor responsible for implementing critical technology solutions across various technical domains to support the firm's business objectives. Our team leverages cutting-edge machine learning techniques alongside the company's unique data assets to optimize business decisions. In this position, you will be part of our industry-leading data analytics team, advancing financial applications from business intelligence generation to predictive models and automated decision-making. You will collaborate closely with financial advisors, investors, client service, and operations.

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 problemsDevelops secure high-quality production code, and reviews and debugs code written by othersIdentifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systemsLeads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architectureLeads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologiesCollaborates with business stakeholders to formulate relevant financial and business questions that can be answered by data analysis.Research's and analyzes data sets using a variety of statistical and machine learning techniques.Communicates final results and give context.Documents approach and techniques used.Works on longer term projects, building tooling that can be used to scale certain types of analyses across multiple datasets and business use cases.Collaborates with internal machine learning teams.
 

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experienceB.S. or M.S. in a quantitative discipline in Computer Science, Mathematics, Statistics, Engineering, Data Science or similarExperience with machine learning APIs and computational packages (examples: TensorFlow, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, statsmodels).Strong ability to develop and debug in Python or similar professional programming language. Experience with big-data technologies and platforms such as Spark, Snowflake, etc.Background and experience in language model fine-tuning and building language models from scratch.Experience with common Generative AI software stack (i.e. HuggingFace, LangChain, FAISS, DSPy, etc.)Must have the ability to design or evaluate intrinsic and extrinsic metrics of your model’s performance which are aligned with business goals.Must be able to independently research and propose alternatives with some guidance as to problem relevance.Must be able to undertake basic and advanced EDA, may require some direction from more senior team; should be aware of limitation and implication of methodology choices.Ensures re-use and sharing of ideas within team and locale.Able to work with non-specialists in a partnership model, conveys information clearly and creates a sense of trust with stakeholders.
 Preferred qualifications, capabilities, and skills PhD In a quantitative discipline