Applied AI ML Lead

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we re setting our businesses, clients, customers and employees up for success

As an Applied AI Machine Learning Lead you will apply sophisticated machine learning methods to complex tasks including natural language processing, speech analytics, and recommendation systems, collaborate with various teams, and actively participate in the knowledge sharing community.You must excel in working in a highly collaborative environment together with the business, technologists, and control partners to deploy solutions into production. You must also have a strong passion for machine learning and invest independent time towards learning, researching, and experimenting with new innovations in the field. You must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.

Job Responsibilities

• Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as NLP, speech recognition and analytics, or recommendation systems
• Choosing, extending, and innovating ML strategies for various banking problems
• Analyzing and evaluating the ongoing performance of developed models
• Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
• Learning about and understanding our supported businesses in order to drive practical and successful solutions

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years of applied experience in Machine Learning and Deep Learning.
• Experience with A/B experimentation and data/metric-driven product development
• Hands-on experience with virtual assistant model development and optimization
• Experience in classical ML techniques including classification, clustering, optimization, cross validation, data wrangling, feature selection, and feature extraction
• Ability to design experiments establish strong baselines, choose meaningful metrics, and evaluate model performance rigorously
• Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
• Good written and spoken communication skills

Preferred qualifications, capabilities, and skills

• Familiarity with continuous integration models and unit test development
• Good understanding of the latest advancement of NLP concepts, such as the transformer architecture and knowledge distillation.