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.
- Contribute to delivery planning, solution design, technical decision-making, and resolution of implementation issues.

Experience

Qualifications and Required Skills

- Typically, six or more years of relevant experience in data science, machine learning, statistical modeling, advanced analytics, AI implementation, or a related field.
- Demonstrated experience implementing analytical, machine-learning, or AI solutions in an enterprise workplace.
- Experience moving solutions from research and prototyping through validation, production deployment, and monitoring.
- Ability to lead complex technical work independently and collaborate effectively across multidisciplinary teams.
- Experience operating within a regulated environment is beneficial.

Technical Expertise

- Strong knowledge of statistical modeling, probability, regression, classification, clustering, forecasting, experimentation, and model validation.
- Advanced proficiency in Python and libraries such as Pandas, NumPy, SciPy, Scikit-learn, Statsmodels, PyTorch, or TensorFlow.
- Strong SQL skill

📌 Lead Data Scientist (Mississauga)
🏢 Citi
📍 Mississauga