Risk, GenAI & Agentic AI)

TD•Boston, MA

1d•$96,130 - $155,950•Onsite

About The Position

The Applied Machine Learning Scientist II is responsible for providing technical knowledge and expertise on advanced analytics and machine learning across a broad range of analytics functions including data and modelling frameworks, tools, technology, processes and procedures. This role generally provides expertise in stakeholder interactions related to complex advanced analytics related material. Additionally, this role plays a lead role in the development of AI/ML systems to solve a range of complex problems and is adept at translating business objectives into technical solutions. The Advanced Analytics (AA) team at TD Bank serves as a Center of Excellence (CoE) delivering advanced analytics, Artificial Intelligence (AI), and Machine Learning (ML) solutions across U.S. business lines. The team partners closely with fraud, risk, operations, digital, and enterprise stakeholders to solve complex business challenges through data-driven innovation. AA is at the forefront of developing scalable AI capabilities that improve operational efficiency, strengthen fraud and risk management, and enhance customer experiences. The team leverages modern cloud-based technologies and advanced AI methodologies — including Generative AI, Agentic AI systems, machine learning, graph analytics, NLP, and predictive modeling — to build intelligent solutions that create measurable business impact. The organization fosters a highly collaborative and innovative environment where scientists work closely with business leaders, engineers, MLOps, governance teams, and enterprise AI partners to transform emerging AI technologies into production-ready enterprise solutions. We are seeking a highly experienced and technically strong Applied Machine Learning Scientist to lead the development of next-generation AI/ML solutions focused on fraud, risk, operational intelligence, and decision optimization. This role is ideal for a senior AI practitioner who combines deep technical expertise with strong business acumen and the ability to lead complex cross-functional initiatives from concept through production deployment. The successful candidate will play a key role in advancing the organization’s capabilities in Generative AI, Agentic AI, machine learning, and intelligent automation. The role requires hands-on expertise in building scalable AI systems while also serving as a technical leader and mentor for junior scientists. Candidates should be comfortable operating in highly ambiguous problem spaces, rapidly prototyping innovative solutions, and collaborating directly with senior business stakeholders to translate strategic priorities into deployable AI products.

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)
Deep expertise in Python, PySpark, SQL, and modern ML/AI frameworks such as PyTorch, TensorFlow, LangChain, LangGraph, Hugging Face, MLflow, or equivalent ecosystems.
Experience working with cloud-based AI/ML platforms such as Azure Databricks and distributed computing environments.
Strong software engineering and productionization skills, including Git-based development workflows, CI/CD concepts, API integration, and scalable AI solution deployment.
Strong understanding of AI governance, explainability, model risk management, and responsible AI principles.
Demonstrated ability to lead complex initiatives and influence cross-functional stakeholders in fast-paced enterprise environments.
Excellent communication and presentation skills with the ability to explain technical concepts to both technical and non-technical audiences.
Proven ability to mentor junior team members and contribute to a strong collaborative team culture.
Strong research mindset with the ability to evaluate and operationalize emerging AI techniques from academic and industry research.

Nice To Haves

Advanced degree (Master’s or PhD preferred) in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or related quantitative discipline.
Extensive experience developing and deploying advanced AI/ML solutions in enterprise environments.
Strong hands-on experience with machine learning, deep learning, NLP, LLMs, Generative AI, and modern AI application architectures.
Experience building or implementing: Agentic AI systems, AI copilots, Retrieval-Augmented Generation (RAG), Prompt engineering frameworks, Multi-agent workflows, Conversational AI solutions, Knowledge retrieval systems.
Experience developing AI/ML solutions within fraud, financial services, risk, payments, or highly regulated industries is strongly preferred.

Responsibilities

Lead the end-to-end development and deployment of advanced AI/ML solutions addressing strategic business challenges across fraud, risk, and operational domains.
Design and implement production-grade machine learning systems using advanced statistical modeling, deep learning, Generative AI, NLP, graph analytics, and Agentic AI frameworks.
Drive innovation in emerging AI capabilities, including: LLM-powered applications, AI copilots and agentic workflows, Retrieval-Augmented Generation (RAG), Multi-agent orchestration frameworks, Intelligent decision support systems, Human-in-the-loop AI solutions.
Develop scalable data science and AI pipelines leveraging technologies such as Python, Databricks, Azure, PySpark, MLflow, vector databases, orchestration frameworks, and modern AI tooling ecosystems.
Partner closely with business leaders, fraud strategy teams, engineering, MLOps, governance, and enterprise AI organizations to identify opportunities and deliver measurable business value.
Translate ambiguous business problems into analytical frameworks, technical solutions, and actionable insights.
Lead technical architecture discussions and contribute to AI platform strategy, solution design, and enterprise AI standards.
Communicate complex analytical concepts and AI solution designs effectively to executive leadership, business stakeholders, and governance partners.
Ensure strong model governance, explainability, monitoring, and responsible AI practices throughout the AI/ML lifecycle.
Mentor and guide junior scientists by promoting best practices in machine learning, software engineering, experimentation, and AI product development.
Maintain awareness of emerging industry trends, academic research, and evolving AI technologies, proactively identifying opportunities to apply them within the organization.