Plan and direct data science / machine learning projects within the team.
Design and implement machine learning models for a number of financial applications including but not limited to: Transaction Classification, Temporal Analysis, Risk modeling from structured and unstructured data.
Measure, validate, implement, monitor and improve performance of both internal and external facing machine learning models.
Propose creative solutions to existing challenges that are new to the company, the financial industry and to data science.
Present technical problems and findings to business leaders internally and to clients succinctly and clearly.
Leverage best practices in machine learning and data engineering to develop scalable solutions.
Identify areas where resources fall short of needs and provide thoughtful and sustainable solutions to benefit the team
Be a strong, confident, and excellent writer and speaker, able to communicate your analysis, vision and roadmap effectively to a wide variety of stakeholders
All About You:
4-7 years in data science/ machine learning model development and deployments
Exposure to financial transactional structured and unstructured data, transaction classification, risk evaluation and credit risk modeling is a plus.
A strong understanding of NLP, Statistical Modeling, Visualization and advanced Data Science techniques/methods.
Gain insights from text, including non-language tokens and use the thought process of annotations in text analysis.
Solve problems that are new to the company, the financial industry and to data science
SQL / Database Experience Is Preferred
Experience with Kubernetes, Containers, Docker, REST APIs, Event Streams or other delivery mechanisms.
Familiarity with relevant technologies (e.g. TensorFlow, Python, Sklearn, Pandas, etc.).
Strong desire to collaborate and ability to come up with creative solutions.
Additional Finance And FinTech Experience Preferred.
Bachelor's or Master's Degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics. M.S preferred
