Senior Data Scientist

Role

• 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