Applied Machine Learning Scientist II (US)

TD•Portland, ME

16h•Onsite

About The Position

This position focuses on the intersection of fairness, transparency, and accountability in Artificial Intelligence and Machine Learning (AI/ML). The successful candidate will contribute to ensuring that models are developed and deployed responsibly, with outcomes that are explainable, trustworthy, and aligned with ethical standards. Financial institutions rely on large-scale datasets and advanced analytics to enhance customer experience and business performance, making rigorous oversight essential. In this position, you will play a key role in evaluating models, addressing emerging challenges related to responsible AI, and supporting the appropriate application of analytical solutions while helping position the organization as a leader in ethical and compliant AI practices. The successful candidate will join the Fair Banking Compliance Model Review team, which is responsible for ensuring the bank’s models are fair, transparent, and responsibly deployed. This role requires strong expertise in Generative AI and machine learning, advanced data analysis, and the selection of appropriate statistical tests and bias metrics, as well as familiarity with emerging fairness methodologies. The incumbent will evaluate existing fairness approaches and contribute to the design and evolution of the bank’s fairness evaluation framework. This is a senior role requiring deep analytical judgment, strong stakeholder management, and effective communication skills, and it plays a critical part in providing independent oversight of models to support fair banking compliance. The role may include people management responsibilities.

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)
Master's degree in computer science, machine learning, AI, engineering, statistics, data science, mathematics or related field with 4+ years of relevant work experience in either developing or validating Generative AI, LLMs, NLP and other Deep Learning models.
PHD with specialization in ML, AI, or Statistics is a plus.
In-depth knowledge of AI/ML methodologies, concepts and theory including Generative AI, Deep Learning, modern Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), Transformers, Diffusion models etc.
Experience with Deep Learning and Generative AI technology stacks and libraries such as PyTorch, PromptFlow, LangChain, HuggingFace, etc.
Motivated to stay up to date with the latest advancements in Generative AI, machine learning, and cloud technologies.
Academic or industry research experience in Responsible AI, fairness, or model evaluation
Proficient in one or more scripting/programming languages such as Python.
Familiarity with cloud platforms (e.g., Azure, AWS) and big data technologies (e.g., PySpark, Hadoop).
Knowledge of machine learning explain-ability/interpretability algorithms.
Excellent verbal and written communication skills and stakeholder management abilities (position requires writing and reviewing reports of a technical nature).
Strong critical and analytic thinking skills.
Excellent time / project management and multitasking skills with minimal supervision.
Ability to work independently and collaboratively in a fast-paced, dynamic environment.
Applicants must have authorization to work in the United States without current or future need for TD sponsorship.

Nice To Haves

Publications in relevant conference and journals and research in GenAI/LLM field is a plus.
Ability to implement AI/ML algorithms from academic research papers is a plus.

Responsibilities

Lead reviews of machine learning, AI, and Generative AI models across TD to identify potential fairness and bias risks.
Represent Compliance in major projects, initiatives, and enterprise engagements.
Perform quantitative analyses to identify fair lending risks associated with model development and use.
Identify and recommend alternative approaches to mitigate fair lending bias arising from models.
Develop specialized analytical tools to support project-specific needs or ongoing review activities.
Compile, analyze, and generate ad-hoc analytical reports for internal stakeholders.
Lead research efforts by applying advanced statistical analysis and modeling expertise.
Provide advisory support to line-of-business model owners on compliance best practices aligned with internal policies and industry standards.
Lead cross-functional teams or initiatives involving significant complexity.
Independently manage end-end functional programs and deliverables.
Apply sound judgment and a sophisticated analytical mindset to identify risks and recommend solutions.
Communicate complex analytical findings clearly to non-technical audiences.
Operate autonomously as a subject matter expert while mentoring and guiding others within the area of expertise.
Manage multiple concurrent projects and priorities in a dynamic business environment.
Lead and mentor junior team members.