Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems Collaborate with multiple partner teams such as Business, Technology, Product Managemen... more details
As an Executive Director on the Machine Learning Center of Excellence (MLCOE) team, you will be responsible for applying advanced machine learning techniques to a variety of complex tasks. These tasks include natural language processing, speech analytics, time series, reinforcement learning, and recommendation systems. You will work closely with different teams and actively contribute to our knowledge sharing community.
In a highly collaborative environment, you will partner with business professionals, technologists, and control partners to implement solutions into production. Your passion for machine learning should be evident, and you should be willing to dedicate time to learning, researching, and experimenting with new innovations in the field. Your expertise in Deep Learning and Large Language Models, coupled with hands-on implementation experience, strong analytical thinking, a deep desire to learn, and high motivation, will be crucial for this role.
Job Responsibilities
Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community
Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems
Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
Required qualifications, capabilities, and skills
PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science, with at least five years of industry or research experience in the field. Or an MS with at least eight years of experience.
Solid background in NLP or speech recognition and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods
Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Curious, hardworking and detail-oriented, and motivated by complex analytical problems
Preferred qualifications, capabilities, and skills
Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development
Knowledge in search/ranking, Reinforcement Learning or Meta Learning
Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code
Strong publication record in Natural Language Processing, Machine Learning, Deep Learning or Reinforcement Learning at major conferences or journals
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