You will be called upon to draw from your research and work experience to help us implement intelligent and practical algorithms. The ideal candidate will have a deep understanding of the techniques, models and best in class practices in machine learning and will have insight into what works best in real-world situations. You will be at the center of prescribing, designing, and building mission-critical solutions.
We're looking for humble, enthusiastic, bright, and personable people with strong communication skills and a deep knowledge of machine learning. We need a proven track record in innovation with strong potential for growth into a leadership position. A background in finance is not a must-have. If you get as excited about machine learning theory as you get about Python and software development, we’d love to speak with you.
Job Responsibilities
- Learning about and understanding our supported businesses in order to drive practical and successful solutions
- Choosing, extending and innovating ML strategies for various banking problems
- Documenting and explaining the rationale and design considerations behind the selection of an ML approach
- Analyzing and evaluating the ongoing performance of developed models to comply with industry regulation
- Collaborating with engineering teams to deploy Machine Learning services that can be integrated with strategic platforms
- Communicating AI capabilities and results to both technical and non-technical audiences
Required Technical Qualifications and Experience
- Masters degree or PhD in a quantitative or computational discipline, with demonstrated expertise in a variety of classical ML techniques, including natural language processing, clustering, optimization, feature selection, classification
- Considerable commercial experience in line with a capable individual contributor
- Strong Python development and debugging skills
- Ability to work both individually and in collaboration with others, and to mentor more junior team members
- Ability to work with non-specialists in a partnership model, conveying information clearly and create a sense of trust with stakeholders
Preferred qualifications, capabilities and skills
- Experience with deep learning frameworks (pytorch, tensorflow)
- Experience with ML development principles (e.g. train-test performance, bias-variance tradeoff) and common techniques (decision trees, clustering, neural networks)
- Experience with natural language processing frameworks (huggingface, spacy, nltk, embeddings, language models)
- Working understanding of MLOps concepts (CI/CD, versioning, reproducibility, development best practices)