What are the responsibilities and job description for the Machine Learning Scientist – Natural Language Processing (NLP) - Vice President position at Kavyos Consulting?
As a Machine Learning Scientist – Natural Language Processing (NLP) - Vice President, you will own the full lifecycle of developing and deploying machine learning solutions, from ideation to production. Acting as a leading voice within Banking on all things Generative AI (GenAI), you will partner closely with all lines of business to innovate new solutions that drive transformational change for the bank. You will actively participate in our knowledge sharing community, representing your work inside and outside of the firm at leading industry conferences amongst peers and leaders in the space. We seek someone who excels in a highly collaborative, fast-paced environment, and holds a strong passion for machine learning to make a significant impact at a leading global financial institution.
Job responsibilities
- Research and develop state-of-the-art machine learning models to solve real-world problems and apply them to tasks involving Generative AI (GenAI)
- Act as a thought partner for Banking leaders and help the business identify and implement new machine learning methods that deliver impact
- Drive cross-functional collaboration with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy, and Business Management to deploy solutions into production
- Lead 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, OR an MS with at least 3 years of industry or research experience in the field
- Solid background in Generative AI (GenAI) and hands-on experience and solid understanding of machine learning and deep learning methods and 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
- 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
Preferred qualifications, capabilities, and skills
- Strong background in Mathematics and Statistics; Familiarity with the financial services industries and continuous integration models and unit test development
- Knowledge in search/ranking 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 or Deep Learning at a major conference or journal