Applied Machine Learning Manager

The Corporate & Investment Bank is a global leader across investment banking, wholesale payments, markets and securities services. The world’s most important corporations, governments and institutions entrust us with their business in more than 100 countries.

As a Trust & Safety employee within the Payments Organization, you will be involved in developing machine learning models and solutions in the business domains of Fraud Prevention (for both Card and ACH/ Wire channels), Sanctions, Onboarding Risk and other similar problems. You will experiment with various relevant AI & ML algorithms and techniques to build best in class solutions. 

Job Responsibilities

• Lead a team of scientists focused on a business problem in the trust & Safety domain


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


• Develop the roadmap of projects while partnering  with various stakeholders for product delivery


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


• Develop solutions and work with partner teams to implement the solutions in production


• Communicate results and ideas to key decision makers


• Hiring and Developing of the team and provide mentoring and coaching 

 Required  Qualifications, Capabilities  and Skills:

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

·         Advanced data mining and EDA (Exploratory Data Analysis) skills, understanding of various data structures and data transformation

·         Knowledge and expertise in Graph ML like Label Propagation, GNN, GCN, Reinforcement Learning apart from understanding supervised & unsupervised learning

.           Expertise in ML in areas of supervised and unsupervised learning. Experience working with imbalanced data.

.           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)

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

 Other experience and qualifications

·         Ability to design or evaluate intrinsic and extrinsic metrics of your model’s performance which are aligned with business goals

·         Ability to independently research and propose alternatives with some guidance as to problem relevance

·         Experience with consumer finance products or domains like working capital, fraud prevention etc.