JOB DESCRIPTION

As a Data Scientist II, you will play a key role in applying data science techniques to support fraud detection strategies and business decision-making. While your primary focus will be on developing and deploying analytical solutions, you may also contribute to broader business initiatives such as system migrations, process improvements, and ad-hoc projects based on evolving business needs. This role requires a balance of technical expertise, problem-solving ability, and flexibility to adapt to changing priorities in a dynamic environment.

JOB REQUIREMENTS:

• Undergraduate degree or 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 to 3 year of relevant experience; higher degree education and research tenure can be counted.
• Proficient in Python for data science(e.g. Pandas, Numpy, Scikit-learn), SQL, and experience working with Databricks or other big data/ML platform
• Experience writing clear, maintainable, and extensible code, with good coding practices for collaboration and long-term sustainability
• Hands-on experience in building and validating machine learning models; model validation experience is preferred, especially in banking or other regulated industries
• Knowledge of fraud detection, risk modeling, or related financial crime domains is a strong asset.
• Strong organizational skills to manage multiple tasks and deadlines.
• Excellent problem solving, analytical, and strategic thinking skills, with the capability to remove obstacles and recommend implementable solutions.
• Effective communication skills, able to explain technical findings in a clear and actionable way to business stakeholders
• Collaborative, proactive, and can-do attitude, with the flexibility to adapt to changing business priorities.
  

  Li-Tech

WHO WE ARE:

TD is one of the world’s leading global financial institutions and is the fifth largest bank in North America by branches/stores. Every day, we deliver legendary customer experiences to over 27 million households and businesses in Canada, the United States and around the world. More than 95,000 TD colleagues bring their skills, talent, and creativity to the Bank, those we serve, and the economies we support. We are guided by our vision to Be the Better Bank and our purpose to enrich the lives of our customers, communities and colleagues.
TD is deeply committed to being a leader in customer experience, that is why we believe that all colleagues, no matter where they work, are customer facing. As we build our business and deliver on our strategy, we are innovating to enhance the customer experience and build capabilities to shape the future of banking. Whether you’ve got years of banking experience or are just starting your career in financial services, we can help you realize your potential. Through regular leadership and development conversations to mentorship and training programs, we’re here to support you towards your goals. As an organization, we keep growing – and so will you.

• Perform data collection, wrangling, profiling, and statistical analysis on fraud-related data to uncover insights and support strategic decision-making.
• Design, develop, and validate fraud detection models, leveraging both traditional machine learning approaches and advanced analytical techniques.
• Build and maintain end-to-end model pipelines with emphasis on scalability, monitoring, and explainability to ensure production readiness and compliance with governance requirements.
• Present insights and model results in a clear and actionable way to business stakeholders.
• Collaborate with partners in analytics, engineering, and fraud strategy teams to deliver high-impact data-driven solutions.
• Monitor model performance and improve processes to reduce false positives, enhance detection rates, and adapt to evolving fraud patterns.
• Stay current on emerging fraud typologies, regulatory changes, and analytical best practices to continuously strengthen fraud detection capabilities.