The data architecture team drives data architecture design, data modeling, AI/ML development and adoption to support data-driven execution across all Commercial Bank business processes. Our work is a blend of ML programming, data product architecture and development, and analytics with a key focus on unlocking data and insights to increase adoption and efficiency by delivering with quality and right controls in place. As a Data Scientist Lead, you will participate in design and development of machine learning programs and collaborate across teams on operation and implementation on top of Hadoop and AWS cloud-based data platforms.

Job Responsibilities 

• A strong passion for AI/ML and analytics with the ability to drive capability development using advanced ML techniques and statistical methodologies.
• Excellent knowledge of commonly used AI/ML methodologies and algorithms in financial service industry and good understanding of data product features and underlying proprietary data, AI/ML algorithms and processes.
•  Leading AI/ML program development, implementation, and communication, in partnership with Data Architecture and Data Engineering teams, to enable new products/features, resolve issues, and make recommendations.
• Improving core competency and processing efficiency via agile process best practice and sustained collaboration with all team members and partners.

Required Qualifications, capabilities, and skills

• Master’s degree in a quantitative field such as mathematics, statistics, computer science or engineering
• Advanced data mining and EDA (Exploratory Data Analysis) skills, understanding of various data structures and data transformation.
• Knowledge and expertise in NLP, Graph ML and Reinforcement Learning apart from understanding supervised & unsupervised learning.
• Strong ability to develop and debug in Python (must) and advanced SQL.
• Strong project management skills – the ability to manage multiple projects and prioritize work requirements to work in a multi-disciplinary team setting.
• 3 years of experience with machine learning APIs and computational packages (e.g., Scikit-Learn, NumPy, SciPy, Pandas).
• 5 years of experience with big-data technologies such as Hadoop, Spark, SparkML, etc.

Preferred Qualifications, capabilities, and skills

• Ability to design or evaluate intrinsic and extrinsic metrics of your ML programs’ performance which are aligned with business goals.
• Ability to independently research and propose alternatives with some guidance as to problem relevance.
• Experience working with commercial data and graph database/programming tools.