Senior Data, AI & Machine Learning ManagerAbout Vistar Analytics
Vistar Analytics is a specialized data, analytics, artificial intelligence, and technology consulting firm helping public-sector organizations, enterprises, and growing businesses turn complex data into practical and measurable outcomes.
Our work spans machine learning, artificial intelligence, predictive analytics, generative AI, business intelligence, geospatial analytics, cloud data platforms, data engineering, and analytics modernization.
We combine technical expertise with strong business and industry understanding to design and deliver solutions that are scalable, explainable, production-ready, and aligned with real operational needs.
As Vistar Analytics continues to expand its Data, AI, and Machine Learning practice, we are seeking a Senior Data, AI & Machine Learning Manager to lead client engagements, guide multidisciplinary technical teams, and deliver advanced analytics and AI solutions from initial discovery through production implementation.
Position Overview
The Senior Data, AI & Machine Learning Manager will lead the end-to-end delivery of artificial intelligence, machine learning, data science, and advanced analytics initiatives for Vistar Analytics clients.
This is a hands-on leadership position for someone who can operate across business strategy, analytics, technical architecture, model development, client engagement, and team leadership.
The successful candidate will work directly with client executives, business leaders, data scientists, machine-learning engineers, data engineers, analytics professionals, and cloud specialists to identify high-value opportunities and translate them into scalable AI and analytics solutions.
The role requires a strong understanding of machine-learning workflows, analytical methods, data requirements, model evaluation, deployment, monitoring, and business-value realization.
In addition to leading delivery, the Senior Manager will help develop Vistar Analytics’ machine-learning and AI capabilities, reusable frameworks, solution offerings, delivery standards, and market presence.
Key ResponsibilitiesMachine Learning, AI, and Advanced Analytics Delivery
- Lead the full lifecycle delivery of machine-learning, artificial intelligence, data science, and advanced analytics solutions.
- Identify and prioritize AI, machine-learning, and analytics use cases based on business value, feasibility, data readiness, implementation effort, risk, and scalability.
- Translate complex business problems into clearly defined analytical questions, machine-learning opportunities, technical requirements, and measurable outcomes.
- Lead projects involving predictive modelling, classification, forecasting, optimization, segmentation, recommendation systems, anomaly detection, natural-language processing, generative AI, and intelligent automation.
- Guide exploratory data analysis, feature engineering, model selection, training, validation, testing, deployment, and performance monitoring.
- Evaluate machine-learning models using appropriate statistical and business performance measures.
- Ensure that models are accurate, explainable, reproducible, secure, maintainable, and suitable for operational use.
- Review analytical methodologies, model assumptions, data transformations, source code, technical documentation, and deployment approaches.
- Provide hands-on technical guidance to data scientists, machine-learning engineers, analytics professionals, and data engineers.
- Support the design and implementation of production-ready analytical applications, decision-support tools, APIs, dashboards, and AI-enabled products.
- Help clients transition machine-learning and AI initiatives from experimentation and proofs of concept into scalable production solutions.
- Establish processes for model monitoring, model retraining, drift detection, performance measurement, version control, and continuous improvement.
- Connect model performance to operational outcomes, user adoption, financial impact, efficiency improvements, and measurable business value.
Data Science and Analytics Leadership
- Define analytical approaches for solving complex business, operational, and policy challenges.
- Lead the development of descriptive, diagnostic, predictive, and prescriptive analytics solutions.
- Establish standards for statistical analysis, data quality assessment, model validation, analytical documentation, and reproducibility.
- Guide teams in selecting appropriate statistical, machine-learning, and analytical methods.
- Ensure that business intelligence, reporting, data science, and AI solutions are aligned within a broader analytics strategy.
- Oversee the development of executive dashboards, analytical products, performance-measurement frameworks, and decision-support solutions.
- Identify patterns, trends, risks, opportunities, and actionable insights from structured and unstructured data.
- Communicate analytical findings through clear narratives, visualizations, presentations, and recommendations.
- Promote the responsible and practical use of analytics and AI in client decision-making.
AI Strategy, Governance, and Responsible AI
- Help clients define practical AI and analytics strategies aligned with their business priorities and technology capabilities.
- Develop roadmaps for adopting machine learning, generative AI, advanced analytics, and intelligent automation.
- Establish responsible AI and machine-learning governance frameworks covering fairness, transparency, explainability, accountability, privacy, security, and regulatory compliance.
- Define model-risk-management processes, including model documentation, independent review, approval, monitoring, change management, and retirement.
- Design operating models for AI and analytics intake, prioritization, funding, delivery, deployment, adoption, and performance management.
- Help clients establish analytics centres of excellence, AI governance committees, delivery teams, and decision-making structures.
- Define policies and controls for the use of sensitive data, third-party models, generative AI systems, and automated decision-making.
- Ensure that AI and machine-learning solutions are aligned with client risk-management, data-governance, privacy, and security requirements.
- Develop performance indicators for model quality, business value, adoption, operational effectiveness, and return on investment.
MLOps and Production Implementation
- Provide direction on MLOps practices and machine-learning lifecycle management.
- Guide the design of repeatable pipelines for data preparation, model training, testing, deployment, monitoring, and retraining.
- Work with data engineers and cloud teams to establish reliable data pipelines and production environments.
- Support the deployment of models through APIs, cloud services, enterprise applications, analytical platforms, and business workflows.
- Promote strong software engineering practices, including version control, testing, code review, documentation, automation, and release management.
- Ensure that machine-learning solutions are scalable, reliable, maintainable, and integrated with operational systems.
- Define monitoring frameworks for model accuracy, data quality, system performance, usage, drift, and business outcomes.
- Help clients establish appropriate development, testing, staging, and production environments for AI and analytics solutions.
Client Engagement and Consulting Leadership
- Lead multiple concurrent client engagements while managing delivery quality, scope, timelines, resources, risks, budgets, and client satisfaction.
- Act as a trusted advisor to senior client executives and leaders across business, technology, analytics, data, operations, and risk functions.
- Facilitate AI discovery sessions, use-case workshops, data-readiness assessments, solution-design discussions, and executive presentations.
- Advise clients on where machine learning and AI can create meaningful value and where traditional analytics or process improvements may be more appropriate.
- Translate technical concepts, machine-learning results, and analytical findings into clear business language.
- Present solution options, recommendations, technical trade-offs, delivery risks, and implementation roadmaps with clarity and credibility.
- Align business stakeholders, technical teams, data owners, risk professionals, and operational users around shared goals.
- Identify delivery challenges early and establish practical plans to address them.
- Maintain trusted client relationships and ensure that engagements deliver measurable and sustainable outcomes.
Technical and Team Leadership
- Lead multidisciplinary teams of data scientists, machine-learning engineers, data engineers, analytics consultants, BI developers, cloud specialists, and business professionals.
- Provide technical direction while remaining actively involved in key analytical and architectural decisions.
- Review machine-learning models, analytical methodologies, solution architectures, data pipelines, dashboards, code, and technical documentation.
- Ensure that appropriate data science, engineering, testing, documentation, governance, and deployment standards are followed.
- Coach team members in machine learning, analytics, problem-solving, technical communication, consulting, and stakeholder management.
- Assign work based on project priorities, technical complexity, delivery timelines, and individual capabilities.
- Provide regular performance feedback, mentorship, and career-development support.
- Contribute to recruiting, onboarding, training, and capability-building initiatives.
- Foster a collaborative environment that encourages technical excellence, experimentation, accountability, and continuous learning.
Business Development and Practice Growth
- Identify opportunities to expand existing client relationships through machine learning, AI, advanced analytics, data engineering, cloud, automation, and business intelligence services.
- Participate in client qualification discussions and shape solutions based on business needs, technical feasibility, data availability, and expected value.
- Lead or contribute to proposals, statements of work, demonstrations, presentations, technical estimates, and RFP responses.
- Define project scope, analytical approaches, architecture, staffing requirements, assumptions, timelines, risks, and commercial estimates.
- Develop reusable machine-learning frameworks, AI accelerators, analytical templates, reference architectures, and delivery methodologies.
- Contribute to thought leadership, technical articles, case studies, industry perspectives, and go-to-market materials.
- Help define new offerings in areas such as generative AI, predictive analytics, machine-learning operations, responsible AI, geospatial AI, and intelligent automation.
- Represent Vistar Analytics in client discussions, industry forums, professional communities, and business-development activities.
- Strengthen Vistar Analytics’ position as a trusted partner for machine learning, AI, data science, and advanced analytics.
Required Qualifications
- Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Analytics, Engineering, Information Systems, Business Administration, or a related field.
- Significant professional experience in machine learning, artificial intelligence, data science, advanced analytics, or analytics consulting.
- Demonstrated experience delivering machine-learning, AI, or advanced analytics solutions from initial discovery through production implementation.
- Experience developing or leading predictive models, forecasting solutions, optimization models, classification systems, segmentation approaches, or other advanced analytical solutions.
- Strong understanding of machine-learning workflows, including data preparation, feature engineering, model training, validation, deployment, monitoring, and retraining.
- Experience translating business challenges into scalable AI, machine-learning, and analytics use cases.
- Proven ability to evaluate model performance, analytical methods, assumptions, data quality, and technical implementation approaches.
- Experience leading client-facing consulting, professional-services, or enterprise analytics engagements.
- Proven ability to work with senior executives and business leaders to define priorities, roadmaps, and measurable outcomes.
- Experience managing multidisciplinary teams across data science, machine learning, analytics, data engineering, software engineering, and business functions.
- Strong understanding of structured and unstructured data, statistical analysis, predictive analytics, and analytical problem-solving.
- Experience managing scope, timelines, resources, risks, dependencies, budgets, and client expectations.
- Strong written, verbal, presentation, facilitation, and stakeholder-management skills.
- Ability to communicate machine-learning and AI concepts clearly to technical and non-technical audiences.
- Ability to work independently, manage competing priorities, and make sound decisions in a growing consulting environment.
Preferred Qualifications
- Experience in a boutique consulting firm, professional-services organization, technology consultancy, or major advisory firm.
- Hands-on experience with Python, SQL, R, Spark, machine-learning libraries, statistical tools, or cloud-based analytics platforms.
- Experience with tools and platforms such as Azure, Microsoft Fabric, Databricks, AWS, Google Cloud, Power BI, MLflow, Azure Machine Learning, or similar technologies.
- Experience with generative AI, large language models, natural-language processing, retrieval-augmented generation, computer vision, or intelligent automation.
- Experience implementing MLOps, automated model pipelines, model registries, monitoring frameworks, or cloud-based model deployment.
- Experience with responsible AI, model governance, explainability, fairness, privacy, security, or model-risk management.
- Experience developing analytical products, APIs, dashboards, executive reporting solutions, or decision-support systems.
- Experience building enterprise AI programs, analytics centres of excellence, data science teams, or AI operating models.
- Experience in public sector, utilities, energy, real estate, property assessment, financial services, transportation, urban planning, public safety, or other data-intensive sectors.
- Experience with geospatial analytics, spatial machine learning, location intelligence, asset analytics, or valuation modelling is considered an asset.
- Experience contributing to proposals, account growth, RFP responses, consulting sales, or solution development.
What Success Looks Like
During the first year, the Senior Data, AI & Machine Learning Manager will be expected to:
- Successfully lead and deliver multiple machine-learning, AI, data science, and analytics engagements.
- Build strong and trusted relationships with senior client stakeholders.
- Convert complex business problems into clearly defined and executable analytical solutions.
- Move AI and machine-learning initiatives from experimentation into practical production implementation.
- Improve the consistency, quality, governance, and scalability of Vistar Analytics’ technical delivery practices.
- Establish reusable frameworks for machine-learning development, AI governance, MLOps, analytics delivery, and value measurement.
- Strengthen the technical, consulting, and client-management capabilities of project teams.
- Contribute to proposals, new client opportunities, account expansion, and solution development.
- Help establish Vistar Analytics as a credible and trusted partner for machine learning, artificial intelligence, data science, and advanced analytics.
Why Join Vistar Analytics
- Lead meaningful machine-learning, AI, and analytics programs from strategy through hands-on implementation.
- Work directly with senior client stakeholders and influence important operational and strategic decisions.
- Help shape the growth, technical capabilities, and service offerings of an expanding consulting practice.
- Work across predictive analytics, generative AI, geospatial analytics, cloud data platforms, and intelligent automation.
- Build practical solutions without the unnecessary layers of a large consulting organization.
- Join an entrepreneurial environment where technical expertise, ownership, innovation, and measurable outcomes are valued.
Job Types: Full-time, Permanent
Pay: $150,000.00-$250,000.00 per year
Benefits:
- Company pension
- Dental care
- RRSP match
Work Location: Hybrid remote in Toronto, ON M5M 3G5
