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Applied Machine Learning Scientist I
TD. Develop, deploy, and maintain Generative AI and predictive machine learning solutions for customer and employee assistance, claims and underwriting support, operational automation, and risk assessment .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and evaluating Generative AI and machine learning solutions, with a strong focus on model performance, compliance with Responsible AI principles, and effective communication of technical results to diverse stakeholders. Proficient in Python and familiar with modern machine learning frameworks, model evaluation methodologies, and data integration techniques.
Highest-signal resume keywords
Python ProficiencyGenerative AI DevelopmentMachine Learning FrameworksModel Evaluation MethodologiesSQL Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Generative AI SolutionsMachine Learning SolutionsModel EvaluationStatistical TestingFeature EngineeringModel Interpretability TechniquesAgent Orchestration FrameworksAPIs IntegrationData ScienceAdvanced Analytics
Soft Skills
Excellent Communication SkillsCollaboration SkillsProblem-Solving SkillsAnalytical SkillsTechnical Translation Skills
Tools & Technologies
Azure CloudAzure ML ServicesDatabricksLangGraphRetrieval-Augmented Generation Pipelines
Industry Keywords
Responsible AI PrinciplesModel GovernanceModel Risk ManagementOperational AutomationRisk Assessment
Tech Stack
Tools & technologiesAzureCloudPythonSQL
About the role
Key responsibilities & impact- Develop, deploy, and maintain Generative AI and predictive machine learning solutions for customer and employee assistance, claims and underwriting support, operational automation, and risk assessment
- Support evaluation, implementation, testing, monitoring, and lifecycle management of internally developed and third-party AI/ML solutions
- Assess vendor-provided and out-of-the-box AI models, including capabilities, limitations, performance characteristics, implementation considerations, and governance implications
- Translate well-defined business problems into analytical frameworks
- Collaborate with cross-functional teams to define success metrics, testing methodologies, and solution approaches
- Contribute to model evaluation, documentation, A/B testing, validation support, and monitoring
- Help ensure model performance, fairness, stability, and compliance with Responsible AI principles
- Communicate technical results to technical and non-technical stakeholders
- Contribute to recommendations regarding model performance, implementation, and risk
- Collaborate with business partners and experienced colleagues to advance AI and Machine Learning adoption at TDI
- Support responsible adoption of internally developed and third-party AI solutions
Requirements
What you’ll need- Excellent written and verbal communication skills
- Ability to collaborate with business, technology, risk, and governance stakeholders
- Ability to translate complex technical concepts and analytical findings into clear business language
- Strong conceptual and problem-solving skills
- Proficiency in Python and modern machine learning frameworks and tools
- Practical experience developing or evaluating machine learning or Generative AI solutions through professional work, internships, research, or substantial academic or personal projects
- Understanding of model evaluation methodologies, experimentation, statistical testing, and performance monitoring
- Familiarity with structured and unstructured data, feature engineering, and model interpretability techniques
- Exposure to LLMs, agentic AI systems, and practical Generative AI applications
- Familiarity with agent orchestration frameworks such as LangGraph
- Understanding of stateful, multi-step agent workflows involving deterministic logic, LLM-driven reasoning, tool calling, conditional routing, memory, or human-in-the-loop controls
- Exposure to integrating LLM applications with APIs, retrieval-augmented generation pipelines, vector stores, structured outputs, or external tools and data sources
- Familiarity with production practices for Generative AI systems, including prompt and workflow versioning, automated evaluation, tracing, observability, guardrails, retry and fallback strategies, or latency and cost considerations
- Familiarity with model governance, Responsible AI principles, model validation, or model risk management practices is an asset
- Experience with SQL, Azure Cloud, Azure ML Services, or Databricks is an asset
- Undergraduate degree in Science, Technology, Engineering, Mathematics, Economics, Finance, or a related quantitative discipline
- 1–3 years of relevant experience in machine learning, advanced analytics, data science, model evaluation, or related fields
- Relevant internships, research, co-op placements, and substantial academic or personal projects may be considered
- For Quebec roles: maîtrise d’une langue autre que le français pour offrir du soutien ou traiter avec des employés ou des collègues qui ont besoin de services et de soutien dans une langue autre que le français
Benefits
Comp & perks- Base salary
- Variable compensation
- Health and well-being benefits
- Savings and retirement programs
- Paid time off
- Banking benefits and discounts
- Career development
- Reward and recognition programs
- Regular development conversations, training programs, and online learning platform
- Mentoring programs
- Training and onboarding sessions
- Interview accommodations, including accessible meeting rooms and captioning for virtual interviews