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.

As a Machine Learning Scientist - Natural Language Processing (NLP) - Senior Associate within our team, you will apply sophisticated machine learning methods to complex tasks including natural language processing, speech analytics, and recommendation systems. You will collaborate with various teams and actively participate in the knowledge sharing community. You should excel in working in a highly collaborative environment together with the business, technologists, and control partners to deploy solutions into production. You should 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 systemsChoosing, extending and innovating ML strategies for various banking problemsAnalyzing and evaluating the ongoing performance of developed modelsCollaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into productionLearning about and understanding our supported businesses in order to promote practical and successful solutions

  

  

Required qualifications, capabilities, and skills 

 BS with 5+ years, or MS with 3+ years of hand-on industry experience in Machine Learning - Deep Learning. Good understanding of the latest advancement of NLP concepts, such as the transformer architecture and knowledge distillation. 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 Solid written and spoken communication skills 

  

Preferred qualifications, capabilities, and skills 

2 years of hands-on experience with virtual assistant model development and optimizationFamiliarity with continuous integration models and unit test development  Experience with A/B experimentation and data/metric-promoten product development