Principal Associate, Data Scientist - Retail Bank Valuations Data Science

About the role

The Marketing & Valuations Data Science Team in the Retail Bank builds models that improve marketing efficiency and drive account growth via intelligent targeting, measurement, segmentation, and customer value modeling. We do data and model pipelining, machine learning, and well-managed model operations using Python and ML libraries in our tech stacks.

Responsibilities

• Work closely with subject matter experts to deliver flexible, well managed models that perform well under a variety of economic conditions
• Translate business goals into data science solutions and communicate with senior stakeholders
• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
• Explore next-generation model architectures (e.g. embeddings, sequence models) to unlock value in our marketing efficiency

Requirements

• Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:
• A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics
• A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 3 years of experience performing data analytics
• A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)

Qualifications

• Technical: Comfortable with open-source languages, hands-on experience with software engineering techniques, and developing end-to-end model pipelines in Python
• Statistically-minded: Experienced in various machine learning algorithms and predictive solutions
• Creative: Thrives on bringing definition to big, undefined problems, loves asking questions, and pushing hard to find answers
• A storyteller: Effective in communicating technical details to a variety of audiences

Preferred Qualifications

• Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)
• At least 3 years’ experience in Python
• At least 3 years’ experience with machine learning, including XGBoost
• At least 3 years’ experience with SQL
• At least 1 year’s experience in open source programming languages for large scale data analysis
• Experience with next-generation model architectures such as embeddings, sequence models