Job Description

J.P. Morgan is a global leader in financial services, providing strategic advice and products to the worlds most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants and employees religious practices and beliefs, as well as any mental health or physical disability needs.

The Corporate & Investment Bank is a global leader across investment banking, wholesale payments, markets and securities services. The worlds most important corporations, governments and institutions entrust us with their business in more than 100 countries. We provide strategic advice, raise capital, manage risk and extend liquidity in markets around the world.

The Global Payment Guardian is Wholesale Payments world class fraud screening application. We are is currently in need of a Machine Learning Engineer, VP to join our fast-growing team. The ideal candidate will be intricately involved in running analytical experiments in a methodical manner and will regularly evaluate alternate models via theoretical approaches. This is a great opportunity for the successful candidate to become a part of an innovative team that analyzes data to develop tools to help fight payment fraud for our clients.

Responsibilities

Collaborate with business, operations and other technology colleagues to understand company needs and devise possible solutions

Research and analyze data sets using a variety of statistical and machine learning techniques

Communicate results and ideas to key decision makers

Document approach and techniques used

Work on longer term projects, building tooling that can be used to scale certain types of analyses across multiple datasets and business use cases

Collaborate with other J.P. Morgan machine learning teams

Keep up-to-date with latest technology trends

Required Technical Qualifications and experience

MS or PhD in a Data Science or related discipline, e.g. Computer Science, Applied Mathematics, Statistics, Physics, Artificial Intelligence

Advanced data mining and EDA (Exploratory Data Analysis) skills

Strong ability to develop and debug in Python (must) and Java (would be a plus)

3+ years experience with machine learning APIs and computational packages (examples: TensorFlow, LightGBM, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, H2O, SHAP, Catboost)

Good experience with model explainability

5 + years of experience with big-data technologies such as Hadoop, Spark, SparkML, etc

Able to understand various data structures and common methods in data transformation

Excellent pattern recognition and predictive modeling skills

Experience with large imbalanced datasets

Other experience and qualifications

Must be curious, hardworking and detail-oriented, and motivated by complex analytical problems

Must have the ability to design or evaluate intrinsic and extrinsic metrics of your models performance which are aligned with business goals

Must be able to independently research and propose alternatives with some guidance as to problem relevance

Experience with fraud detection

Should be able to work both individually and collaboratively in teams, in order to achieve project goals

Able to work with non-specialists in a partnership model, conveys information clearly and creates a sense of trust with stakeholder

Ensures re-use and sharing of ideas within team and locale