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Senior Data Scientist - Generative AI, ML & Agentic Systems
Scotiabank —Toronto, ON
Effectively communicate complex technical findings, project progress, and strategic recommendations to both technical and non-technical stakeholders.
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Senior Data Scientist - Generative AI, ML & Agentic Systems

Scotiabank
Toronto, ON

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Job details

Skills

CI/CD
Azure
Data mining
Law
Doctoral degree
Computer Science
Project planning
NumPy
Git
Google Cloud Platform
Master's degree
SQL
Pandas
Statistical analysis
Project management
Docker
Presentation skills
Machine learning
Scripting
Business requirements
AI
Leadership
Communication skills
Python
Data Science

Full job description

Requisition ID: 256850

Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.

The Role

We are seeking a passionate and experienced Senior Data Scientist to implement various AI and Machine Learning Solutions. This is a unique opportunity to make a significant real-world impact by enhancing customer experiences and operational efficiency through innovative, data-driven solutions. You will play a pivotal role in the execution and implementation of next-generation AI systems. As a Senior Data Scientist, you will mentor and guide a talented group of data scientists, collaborating with experts from various fields, including data engineers, software developers, and business analysts, to apply LLM-driven and agentic AI solutions across data-rich banking operations.

Is this role right for you? In this role, you will be involved in:

Team Leadership and Mentorship:

Lead, mentor, and develop a high-performing team of data scientists, fostering a culture of innovation and collaboration.

Manage data science projects from conception to deployment, ensuring they are delivered on time and meet business requirements.

Provide technical guidance and oversight on the development and implementation of a diverse portfolio of analytics models, spanning both classical machine learning and generative AI.

Technical Strategy and Execution:

Determine the most effective AI/ML approach for various business problems, choosing between traditional models and generative AI solutions based on performance, cost, and interpretability requirements.

Oversee the end-to-end lifecycle of traditional machine learning models (e.g., for fraud detection, credit risk, and customer churn), from feature engineering and model selection to deployment, monitoring, and maintenance.

Lead the development and fine-tuning of traditional AI and ML models for diverse banking use cases.

Lead the implementation of conversational AI solutions, ensuring they deliver a seamless and intuitive user experience.

Drive the development of scalable and robust agentic architectures, enabling autonomous and intelligent decision-making within our AI systems.

Lead the deployment of Large Language Models (LLMs) for a wide array of banking applications beyond customer interfaces, such as automated content generation, risk assessment insights, and internal knowledge management.

Analysis and Insights:

Guide the analysis of interactions with LLM-powered agents and RAG systems to identify emergent patterns, user needs, and critical areas for performance optimization.

Generate and present actionable insights derived from both traditional and generative AI analysis to strategically inform senior management and drive business decisions.

Establish and oversee the evaluation of model performance using relevant metrics, developing strategies for model optimization and bias mitigation across all models.

Direct the continuous monitoring, updating, and re-training of all models to adapt to changing market dynamics and evolving data landscapes.

Documentation and Communication:

Maintain comprehensive documentation of data sources, model development (for both traditional ML and GenAI), and implementation processes.

Provide regular reports and updates on AI solution performance and project progress to senior management and stakeholders.

Work closely with cross-functional teams to seamlessly integrate cutting-edge AI solutions into core banking operations.

Effectively communicate complex technical findings, project progress, and strategic recommendations to both technical and non-technical stakeholders.

Governance and Quality Assurance:

Ensure all AI solutions, including traditional predictive models and generative systems, comply with banking regulations and ethical AI standards, with a focus on model explainability, fairness, and responsible use.

Oversee rigorous testing, validation, and quality assurance for all AI solutions to ensure accuracy, fairness, and a seamless customer experience.

Proactively identify and mitigate potential risks and challenges that arise during the development and deployment of advanced AI systems.

Do you have the skills that will enable you to succeed? We’d love to work with you if you have the following:

Master's or Ph.D. in Data Science, Computer Science, Statistics, or a related field.

Proven experience in a data science leadership role, with a track record of successfully leading and delivering data science projects.

Extensive experience in Python and its core data science libraries (e.g., Scikit-learn, Pandas, NumPy, Matplotlib/Seaborn) for data analysis, modeling, and scripting.

Strong expertise in SQL for data manipulation and querying.

Strong theoretical and practical knowledge of classical machine learning algorithms (e.g., classification, regression, clustering, dimensionality reduction) and their applications in areas such as fraud detection, credit risk scoring, or customer segmentation.

Demonstrated expertise in feature engineering, feature selection, and data transformation techniques for structured and unstructured data.

Solid understanding of statistical analysis, experimental design (e.g., A/B testing), and statistical hypothesis testing to rigorously evaluate model performance and impact.

Extensive hands-on experience with Large Language Models (LLMs), including fine-tuning, prompt engineering, and a deep understanding of their capabilities and limitations.

Proven experience with Retrieval Augmented Generation (RAG) architectures and their implementation for grounded, factual AI responses.

Experience with Agentic AI frameworks and designing multi-step AI reasoning processes.

Experience with MLOps principles and tools for model versioning (e.g., Git), containerization (e.g., Docker), and continuous integration/continuous deployment (CI/CD) of machine learning models.

Proficiency in data mining, data profiling, modeling, cleansing, and enriching.

Demonstrable experience with Google Cloud Platform (GCP) or Microsoft Azure.

Experience with extract, transform, load (ETL) solutions.

In-depth knowledge of data privacy and security standards, especially when handling sensitive customer data within generative AI applications.

Excellent communication and presentation skills, with a proven ability to interact effectively with clients, vendors, and senior management.

Strong project management skills with the ability to prioritize tasks, plan, and manage projects effectively in a fast-paced environment.

Familiarity with Microsoft Foundry and/or Google's Vertex AI is a nice to have.

Working Conditions

Work in a standard office-based environment; non-standard hours are a common occurrence

Location(s): Canada : Ontario : Toronto

Scotiabank is a leading bank in the Americas. Guided by our purpose: "for every future", we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.

At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.

Apply Now

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