This position is responsible for technical guidance and execution of data science projects and approaches/methodologies that improve analytic capabilities. This individual will work with our business partners, Advanced Analytics teams and external partners to identify opportunities where data science models can help produce better decision-making and outcomes for business and technology teams. The goal of this position is to identify and gain adoption of new concepts and technology in the areas of conversational AI, content analytics, fraud detection, and others. The individual is expected to lead and apply his/her technical expertise by applying and adopting new algorithms, especially deep learning approaches, and understanding architectural frameworks needed to bring solutions into production.
- Identify and elaborate data science opportunities by working closely with business partners and external experts
- Develop prototypes and approaches that leverage advanced statistical and machine learning algorithms, including neural networks and deep learning.
- Perform feature engineering from large, complex datasets.
- Work with the data development and architecture teams to design solutions for launching successful analytics prototypes into production.
- Work in an agile and iterative manner to foster innovation.
- Work closely with different stakeholders and business functions (e.g., Marketing and Client Education) within TD Ameritrade to derive important business insights and decide how data science can help achieve objectives.
- Present to TDA stakeholders to demonstrate capabilities, value proposition, alignment to strategy, and how data science solutions can be deployed within the firm.
- Collaborate with application development, universities, and Advanced Technology Group to identify and develop new analytical capabilities for the firm.
- Document technical design and data engineering approaches.
- Help develop a communication strategy for the launch and adoption of new capabilities.
- Participate in Think-Offs and Hackathons to demonstrate disruptive capabilities.
- Attend and present in data science conferences and meetups, and contribute to industry trends as it relates to advanced analytics and data science.