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Applied AI/ML - Sr. Associate



Software Engineering, Data Science
Columbus, OH, USA
Posted on Tuesday, July 2, 2024

Job Description

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 an Applied AI Machine Learning Lead within our dynamic team, you will be responsible for applying advanced machine learning techniques to intricate tasks such as natural language processing, speech analytics, and recommendation systems. Your role will involve active collaboration with various teams and participation in our knowledge sharing community. You will thrive in a highly collaborative environment, working closely with business professionals, technologists, and control partners to implement solutions into production. Additionally, your strong passion for machine learning will promote you to independently invest time in learning, researching, and experimenting with new innovations in the field.

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

  • MS with 5+ years, or PhD with 4+ years of hand-on industry experience in Machine 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

  • 5 years of hands-on experience with virtual assistant model development and optimization
  • Familiarity with continuous integration models and unit test development
  • Experience with A/B experimentation and data/metric-driven product development