GENERAL DESCRIPTION

The Data Scientist within the Markets Data Analytics team performs quantitative research and develops mathematical models in the areas of trading & sales, execution, algorithmic trading, and artificial intelligence.

A Data Scientist in the team is expected to write structured code, document results, and present findings to product owners and stakeholders. Furthermore, they support in developing Markets use cases and will be playing a key part in developing including scalable quantitative and machine learning models. The team consists of Data scientists, Data Engineers and Developers applying DataOps and MLOps to build and maintain the Markets Data Analytics Platform (DAP).

KEY FEATURES AND ACCOUNTABILITIES

Role & Responsibilities

• Ensure data quality, including mining, cleansing, analyzing and structuring data to build models and use cases
• Understand and code Machine Learning models relevant for use-case development
• Perform quantitative research and develop mathematical models in the areas of trading & sales, execution, algorithmic trading and artificial intelligence
• Write structured code, document results, and present findings to product owners and stakeholders
• Automate the evaluation of AI model quality metrics
• Prototype new capabilities using new AI models
• Use Python/PySpark to interact with and evaluate AI models
• Design and execute experiments on new AI features and models
• Develop models of usage, user behavior & business behavior to make recommendations and influence the product road map

Regulatory Responsibilities &/OR Risk Management

• Demonstration of appropriate values and behaviours including but not limited to standards on honesty and integrity, due care and diligence, fair dealing (treating customers fairly), management of conflicts of interest, competence and continuous development, adequate risk management, and compliance with applicable laws and regulations

SKILLS REQUIREMENTS

Professional and Technical

• Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR
• Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR
• Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
• Prior relevant technical experience in working with big data, modern data analysis techniques and/or tools with understanding of math/statistics fundamentals
• Developing for continuous integration and automated deployments
• Familiarity with banking and financial services use cases
• Passion for data and experience in direct application in data driven business
• Knowledge on machine learning for data mining, clustering and predictive analysis is a plus
• Proficiencies with common tools like Excel, PowerBI
• Experience with MLFlow and Databricks
• Experience with Azure and MLOps

Personal and Social

• Proficient strong communication skills with the ability to decompose complex solutions to articulate data insights and vision into a compelling data storytelling
• Able to work independently and in a global team setup to take on initiatives with a positive attitude
• Ability to work in a fast-paced environment with the occasional priority changes and context switches
• A strong team player
• Positive thinking personality with a can-do attitude
• Self-motivator, able and willing to work with minimal direction