Principal Data Scientist (US)

TD Bank•Southfield, MI

2d•$148,720 - $223,080

About The Position

The Principal Data Scientist collects and analyzes data using techniques such as data wrangling, profiling, visualization, statistical inference, and machine learning to uncover actionable insights, build analytics solutions, guide decisions, and support strategic planning. The role provides technical expertise on bank-wide and industry-wide solutions that address capability gaps and create customer value. It owns end-to-end decisioning and pricing optimization engines and uses historical and market data to form hypotheses, conduct analyses, develop and evaluate models, implement solutions, and inform business decisions. The position leads complex, high-profile, and high-risk initiatives autonomously, primarily at the enterprise level. It provides analytical and technical leadership, consultative guidance to executive leaders, and direction to other data science professionals. The Principal Data Scientist partners with business leaders and cross-functional teams to identify needs, shape strategies, build scalable solutions, and manage risks while keeping current with industry, analytical, and regulatory developments. The role is based in Southfield, Michigan, and is listed at 40 hours. The stated base salary range is $148,720–$223,080 USD. TD describes a total rewards package that includes base and variable compensation, health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and recognition. The company also describes training, onboarding, career and development conversations, online learning, and mentoring programs.

Requirements

Undergraduate degree or advanced technical degree preferred, such as in mathematics, physics, engineering, finance, or computer science.
Graduate degree preferred, with progressive project work experience; alternatively, 10+ years of relevant experience. Higher-degree education and research tenure may be counted.
Expertise in data science concepts, principles, practices, standards, analytical concepts, and techniques.
Experience using programming languages such as Python and techniques for extracting and preparing data, applying statistics, and conducting advanced analytics.
Ability to understand business context and data infrastructure and translate business problems into data science solutions.
Ability to analyze large-scale historical and market data and develop, evaluate, and implement machine learning and statistical models.
Strong business acumen and ability to communicate analytical results to stakeholders with varying levels of technical understanding.
Ability to lead complex projects autonomously, influence partners, and provide technical and strategic guidance to executive leadership.
Ability to collaborate across business and technical functions and develop scalable, sustainable solutions.
Knowledge of risk management, enterprise frameworks or methodologies, and applicable regulatory requirements.
Ability to mentor peers and contribute to team development.

Nice To Haves

Graduate degree.
Advanced technical degree in mathematics, physics, engineering, finance, or computer science.
Progressive project work experience or research tenure in lieu of some relevant experience.

Responsibilities

Own end-to-end decisioning and pricing optimization engines, using historical and market data to form hypotheses, conduct analyses, develop and evaluate machine learning models, implement models, and drive business decisions.
Analyze large-scale data and apply statistical models to recommend insights and address business objectives.
Collaborate with business partners to identify and refine strategies, analyze alternative deal structures, and deliver business P&L expectations.
Stay current on industry trends and optimization methodologies in auto finance markets.
Lead analytical projects autonomously and deliver analytical presentations to executive audiences and stakeholders.
Guide business leaders in prioritizing needs and provide impactful analytical insights and strategic guidance.
Set direction and oversee other data science professionals on complex, highly visible, strategically important projects with enterprise impact.
Translate business problems into viable data science solutions and explain insights through visualizations and clear storytelling for decision-makers.
Collaborate with data and business analysts, software engineers, data engineers, application developers, and business process owners to build scalable, sustainable analytical solutions.
Provide technical leadership and expertise in specialized areas and lead strategic development efforts on new or emerging data science projects.
Provide analytical thought leadership, identify emerging analytical trends and data needs, and create repeatable analytical solutions and models.
Identify and manage risks, escalate non-standard high-risk activities, and implement remediation plans for performance, risk, or governance issues.
Extract knowledge and insights from structured and unstructured data using appropriate mathematical, statistical, and analytical methods.
Keep abreast of emerging issues, trends, and regulatory requirements; assess potential impacts and maintain risk management and control practices.
Participate in team knowledge transfer, mentor colleagues, share best practices, support team development, and keep stakeholders informed of project progress.
Build productive relationships with internal and external partners and contribute to a fair, positive, and equitable workplace.
Travel domestically occasionally; international travel is listed as never.

Benefits

Health and well-being benefits
Savings and retirement programs
Paid time off, including Vacation PTO, Flex PTO, and Holiday PTO
Banking benefits and discounts
Career development
Reward and recognition
Training and onboarding
Online learning platform
Mentoring programs
Competitive benefits plan
Variable compensation/incentive awards, including potential cash and/or equity awards