The Lead Data Scientist will work hands-on across the AI and analytics development lifecycle. The role will design analytical approaches, develop and validate models, build Generative AI and agentic workflows, and partner with data engineers and technology teams to implement scalable production solutions.

The successful candidate will combine robust data-science expertise with practical engineering skills, enterprise implementation experience, and an understanding of governance requirements. The role will also provide technical guidance to other data scientists and help establish reusable methods, coding standards, and implementation practices.

Key Responsibilities

Advanced Analytics and Statistical Modeling

- Design and implement statistical analyses, predictive models, machine-learning solutions, and analytical frameworks.
- Translate workforce and organizational questions into measurable hypotheses, appropriate methodologies, and actionable insights.
- Apply techniques such as regression, classification, clustering, survival analysis, forecasting, experimentation, and causal inference.
- Perform feature engineering, variable selection, model tuning, validation, sensitivity testing, and performance evaluation.
- Apply explainability methods such as SHAP, feature attribution, and scenario analysis.
- Clearly document and communicate analytical assumptions, limitations, findings, and business implications.

Generative AI and Agentic Solutions

- Design and build Generative AI applications, Retrieval-Augmented Generation solutions, and agentic workflows.
- Implement tool calling, workflow orchestration, structured outputs, multi-turn interactions, and state management.
- Integrate foundation models through approved enterprise gateways and cloud platforms.
- Develop reusable prompts, agents, tools, evaluation components, and analytical services.
- Build evaluation frameworks to measure accuracy, groundedness, retrieval quality, task completion, consistency, and hallucination risk.
- Test and optimize solutions for reliability, performance,



latency, cost, and user experience.

Data Engineering and Solution Implementation

- Partner with data engineers and architects to design data pipelines, analytical datasets, feature-generation processes, APIs, and production integrations.
- Develop clean, reusable, and well-documented Python and SQL code for analytical and AI solutions.
- Work with structured and unstructured data from multiple enterprise sources.
- Implement data-quality checks, logging, monitoring, error handling, and traceability within analytical workflows.
- Support the development of feature libraries, embedding pipelines, vector-search capabilities, and model services.
- Move prototypes into scalable production solutions using established MLOps and LLMOps practices.

Governance and Responsible AI

- Ensure solutions comply with applicable Model Risk Management, privacy, cybersecurity, data-governance, and responsible AI requirements.
- Implement appropriate access controls, data entitlements, PII protections, cohort-suppression rules, and analytical safeguards.
- Conduct model-performance, explainability, bias, fairness, stability, and outcome testing.
- Support controls for prompt-injection detection, approved tool use, source grounding, and model-output validation.
- Prepare documentation covering methodologies, data dependencies, testing, limitations, controls, and implementation decisions.
- Partner with risk and control functions to address findings and support required reviews.

Technical Leadership and Collaboration

- Serve as a technical lead for complex data-science and AI initiatives while remaining a hands-on individual contributor.
- Provide technical guidance and peer review to data scientists working on related solutions.
- Help establish standards for coding, analytical design, model validation, testing, documentation, and deployment.
- Partner with product managers, data engineers, architects, technology teams, and business stakeholders throughout implementation.
- Present complex technical concepts and analytical results in clear language for business and senior stakeholders.

📌 Lead Data Scientist (Canada)
🏢 Citi
📍 Canada