Machine Learning Scientist - Natural Language Processing (Nlp) - Vice President - Machine Learning Center Of Excellence

JobID: 210495180

Category: Predictive Science

JobSchedule: Full time

Posted Date: 2024-05-02T13:53:10+00:00

JobShift: Day

Base Pay/Salary: New York,NY $147,250.00-$260,000.00; Seattle,WA $147,250.00-$260,000.00; Palo Alto,CA $147,250.00-$260,000.00

Explore the transformative potential of AI and ML with opportunities at Sr. Associate, Vice President, and Executive Director level in New York, Palo Alto, and Seattle, WA. As a Machine Learning Scientist

• NLP - Vice President, you'll apply sophisticated machine learning methods to complex tasks, working collaboratively with business, technologists, and control partners. This role requires a passion for machine learning, a strong analytical mindset, and expertise in Deep Learning.

As a Machine Learning Scientist

• Natural Language Processing (NLP) - Vice President in the Machine Learning Center of Excellence, you will have the unique opportunity to apply sophisticated machine learning methods to a wide variety of complex tasks. You will work in a highly collaborative environment with business, technologists, and control partners to deploy solutions into production. Your passion for machine learning will promote you to invest independent time towards learning, researching, and experimenting with new innovations in the field. You will also have the opportunity to promote firm-wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business.

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 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 three years of industry experience Or an MS with at least five years of industry or research experience in the field.

• 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

• Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal