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Applicants must be currently authorized to work in Canada on a full-time basis. The employer will not sponsor applicants for work visas.
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Assistant Manager
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Senior Engineering and Delivery Lead
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Citi

Lead Data Scientist / VP

Mississauga, ON, Canada

$111,600.00 - $161,600.00 · Full-time

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Job details

Pay: $111,600.00 - $161,600.00
Job type: Full-time
Apply by: Nov 8, 2026
Experience: 6+ Years of experience in Engineering or HR Technology.
Posting language: English
Posted: Oct 30, 2026

Original job description

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

Qualifications and Required Skills

Experience

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 environment.
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 skills and experience with data extraction, transformation, analytical cohort development, and data-quality testing.
Experience designing or implementing Generative AI, Retrieval-Augmented Generation, tool-calling, or agentic applications.
Familiarity with frameworks such as Google Agent Development Kit, LangGraph, LangChain, CrewAI, or comparable technologies.
Experience integrating foundation models through enterprise AI platforms such as Google Cloud and Vertex AI, Microsoft Azure, or AWS.
Understanding of APIs, data pipelines, vector embeddings, semantic search, and production integration patterns.
Familiarity with MLOps or LLMOps practices, including versioning, testing, deployment, observability, monitoring, and change management.
Knowledge of responsible AI, model explainability, bias and fairness testing, data privacy, and role-based access controls.

Beneficial Experience

Experience in People Analytics, Workforce Planning, Human Capital Management, organizational effectiveness, or financial-services analytics.
Experience working with sensitive workforce data and governed analytical environments.
Familiarity with enterprise software-development practices, source control, automated testing, and CI/CD.
Strong written and verbal communication skills, including the ability to explain technical methods to non-technical audiences.

Education

Master’s degree or Ph.D. in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Operations Research, or another quantitative discipline.
A bachelor’s degree with substantial relevant technical and implementation experience may also be considered.

Master's degree in Analytics, Data Science, Computer Science.

6+ Years of experience in Engineering or HR Technology.

Experience with Python and JavaScript

AI Automation.

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Job Family Group:

Decision Management

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Job Family:

Specialized Analytics (Data Science/Computational Statistics)

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Time Type:

Full time

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Primary Location Full Time Salary Range:

$111,600.00 - $161,600.00

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Most Relevant Skills

Please see the requirements listed above.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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Automated Processing and AI

We use automated processing, including artificial intelligence, for our legitimate business interests (or our reasonable and appropriate business purposes) to identify and align the candidate's skills and abilities with a specific job opening. Additionally, if you so choose, or consent, we can match your skills and abilities to other suitable roles at Citi.

Importantly, all our hiring processes and decisions, including determining your suitability for a role, are conducted, checked, and decided by individuals. Our automated processing and AI do not involve relying on automatic or autonomous decision-making. Please refer to any Jurisdictional Considerations, with specific provisions for your country (where relevant) for further details.

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This job opening is for an existing job vacancy.

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Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.

If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi.

View Citi’s EEO Policy Statement and the Know Your Rights poster.

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More about this job

Responsibilities

The Lead Data Scientist will design and implement advanced analytical models, Generative AI solutions, and agentic workflows while partnering with engineering teams for production deployment. The role also involves providing technical leadership, establishing coding standards, and ensuring compliance with governance and responsible AI requirements.

Requirements

Candidates must have six or more years of experience in data science, machine learning, or AI implementation within an enterprise environment. A Master’s degree or Ph.D. in a quantitative discipline is required, along with advanced proficiency in Python, SQL, and modern AI frameworks.

Skills

Python
SQL
Machine Learning
Statistical Modeling
Generative AI
Retrieval-Augmented Generation
Agentic Workflows
Data Engineering
MLOps
LLMOps
Predictive Modeling
Data Governance
Cloud Platforms
Model Validation
Feature Engineering
Technical Leadership

Visa sponsorship

Not detected in the job text

Keywords

Data Science
Machine Learning
Generative AI
Python
SQL
Statistical Modeling
Retrieval-Augmented Generation
Agentic Workflows
MLOps
LLMOps
Data Engineering
Cloud Platforms
Vertex AI
LangChain
LangGraph
PyTorch
TensorFlow
Pandas
NumPy
SciPy
Scikit-learn
Statsmodels
Model Risk Management
Responsible AI
Data Governance
Predictive Analytics
Feature Engineering
Vector Search
CI/CD
People Analytics

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