Technical Product Owner (Layer 6) - AI/ML Project Delivery

TD•New York, NY

2d•$96,130 - $181,944

About The Position

Department Overview: Layer 6 is the AI research center of excellence for TD Bank Group. We develop and deploy industry-leading machine learning systems that impact the lives of over 27 million customers, helping more people achieve their financial goals and needs. Our research broadly spans the field of machine learning with areas such as deep learning and generative AI, time series forecasting and responsible use of AI. We have access to massive financial datasets and actively collaborate with world renowned academic faculty. Position Overview: Day-to-day as a Technical Product Owner Translate broad business problems into sharp data science use cases, and craft use cases into product visions Own machine learning products from vision to backlog; prioritizing features and defining minimum viable releases; maximizing the value your products generate, and the ROI of your pod Guide Agile pods on continuous improvement, ensuring that the next sprint is delivered better than the previous Work closely with stakeholders to identify, refine and (occasionally) reject opportunities to build machine learning products; collaborate with support functions such as risk, technology, model risk management and incorporate interfacing features Facilitate the professional & technical development of your colleagues through mentorship and feedback Anticipate resource needs as solutions move through the model lifecycle, scaling pods up and down as models are built, perform, degrade, and need to be rebuilt Championing model development standards, industry best-practices and rigorous testing protocols to ensure model excellence Self-direct, with the ability to identify meaningful work in down times and effectively prioritize in busy times Drive value through product, feature & release prioritization, maximizing ROI & modelling velocity Be an exceptional collaborator in a high-interaction environment The TPO (Machine Learning Engineer II) is responsible for providing technical expertise as well as developing and maintaining technical solutions that adhere to engineering and architectural design principles while meeting business requirements. This role plays a lead role in implementing solutions with a focus on efficiency, reliability, scalability, and security; includes planning, evaluating, recommending, designing, operationalizing, and supporting machine learning engineering solutions & systems in compliance with enterprise and industry standards. Depth & Scope: Expert knowledge of specific domain or range of engineering frameworks, technology, tools, processes, and procedures, as well as organization issues Expert knowledge of TD applications, systems, networks, innovation, design activities, best practices, business / organization, Bank standards, and may fulfill a governance role Integrates knowledge of business and functional priorities and acts as a key contributor in a complex and critical environment Adept at technical project execution, strategic planning, and effective communication. Accountable for specialized knowledge in a field of AI/ML Engineering and may provide leadership to teams or projects, shares expertise Applies in-depth skills and broad knowledge of the business to address complex problems and nonstandard situations

Requirements

Undergraduate degree required, advanced technical degree preferred (e.g., math, physics, engineering, finance or computer science)
Graduate's degree preferred with either progressive project work experience or 1 +years relevant experience (includes post graduate experience)
Expert knowledge of specific domain or range of engineering frameworks, technology, tools, processes, and procedures, as well as organization issues
Expert knowledge of TD applications, systems, networks, innovation, design activities, best practices, business / organization, Bank standards, and may fulfill a governance role
Integrates knowledge of business and functional priorities and acts as a key contributor in a complex and critical environment
Adept at technical project execution, strategic planning, and effective communication.
Accountable for specialized knowledge in a field of AI/ML Engineering and may provide leadership to teams or projects, shares expertise
Applies in-depth skills and broad knowledge of the business to address complex problems and nonstandard situations

Nice To Haves

Proven professional track record of delivering major data science, AI/ML projects in large, complex organizations
Strong communication, business acumen and stakeholder management competencies
Strong technical skills: machine learning, data engineering, MLOps, cloud solution architecture, software development practices
Strong coding proficiency: python, R, SQL and / or Scala, cloud architecture
Certified Scrum Product Owner and / or Certified Scrum Master or equivalent experience
Familiarity with cloud solution architecture, Azure a plus
Master’s degree in data science, artificial intelligence, computer science or equivalent experience

Responsibilities

Translate broad business problems into sharp data science use cases, and craft use cases into product visions
Own machine learning products from vision to backlog; prioritizing features and defining minimum viable releases; maximizing the value your products generate, and the ROI of your pod
Guide Agile pods on continuous improvement, ensuring that the next sprint is delivered better than the previous
Work closely with stakeholders to identify, refine and (occasionally) reject opportunities to build machine learning products; collaborate with support functions such as risk, technology, model risk management and incorporate interfacing features
Facilitate the professional & technical development of your colleagues through mentorship and feedback
Anticipate resource needs as solutions move through the model lifecycle, scaling pods up and down as models are built, perform, degrade, and need to be rebuilt
Championing model development standards, industry best-practices and rigorous testing protocols to ensure model excellence
Self-direct, with the ability to identify meaningful work in down times and effectively prioritize in busy times
Drive value through product, feature & release prioritization, maximizing ROI & modelling velocity
Be an exceptional collaborator in a high-interaction environment
Providing technical expertise as well as developing and maintaining technical solutions that adhere to engineering and architectural design principles while meeting business requirements
Implementing solutions with a focus on efficiency, reliability, scalability, and security; includes planning, evaluating, recommending, designing, operationalizing, and supporting machine learning engineering solutions & systems in compliance with enterprise and industry standards