RQ11452 – Data Scientist – Senior

We are seeking a Senior Data Scientist to support the Ministry of Public and Business Service Delivery and Procurement in Toronto, Ontario. This onsite role will analyze complex structured, semi-structured, unstructured, spatial, non-spatial, historical, and legacy datasets to develop predictive models, analytical frameworks, business-intelligence solutions, and geospatial insights. The successful candidate will combine strong statistical analysis, machine learning, programming, data management, data governance, spatial analytics, visualization, and business-partnering skills. The role will support planning, compliance, reporting, data modernization, digitization, information management, and evidence-based decision-making. Position snapshot Job Title: RQ11452 — Data Scientist — Senior Client: Ministry of Public and Business Service Delivery and Procurement Location: Toronto, Ontario Work Arrangement: Onsite Onsite Requirement: Five days onsite per week Estimated Start Date: October 26, 2026 Estimated End Date: October 5, 2027 Business Days: 240 Extension: 125 business days Hours: 7.25 hours per day Security Level: No clearance required Application Deadline: Tuesday, September 8, 2026, at 10:00 AM EST Application Emails: [email protected] or [email protected] Mandatory application requirements The Following Documents Are Required Updated resume in Word format. References. Expected hourly rate. Visa status. LinkedIn ID. Applications without all mandatory documents cannot be submitted. Key responsibilities Data science and predictive analytics Analyze complex datasets to generate predictive models and insights that inform business strategy. Apply statistical methods, machine-learning techniques, mathematical concepts, and programming languages to interpret data. Develop actionable insights to support planning, compliance, reporting, program delivery, and decision-making. Initiate, research, develop, and manage new information studies. Design innovative statistical models and analytical approaches for complex data challenges. Conduct exploratory and initial data analyses to understand data characteristics. Identify patterns, trends, relationships, anomalies, and opportunities within large datasets. Evaluate model results, assumptions, limitations, accuracy, and business relevance. Translate analytical findings into practical recommendations for business and program stakeholders. Business problem analysis Identify business problems that can be addressed through data analysis, predictive modeling, business intelligence, or geospatial techniques. Identify relevant data sources and datasets to support client business needs. Gather and assess large structured and unstructured datasets and related variables. Define data requirements, analytical objectives, measures, assumptions, and expected outcomes. Collaborate with stakeholders to ensure analytical solutions address real business and program requirements. Develop data-driven solutions that improve business performance, service delivery, planning, and decision-making. Integrate data insights into broader business strategies and operational plans. Data integration and management Integrate data from multiple sources, platforms, repositories, and information systems. Work with relational databases, data warehouses, data lakes, and other data-management environments. Support extract, transform, and load processes and data-transformation activities. Analyze and manage structured, semi-structured, unstructured, historical, and legacy datasets. Support data migration, digitization, modernization, records conversion, and information-management initiatives. Perform data profiling, validation, cleansing, reconciliation, transformation, and quality assessment. Identify and document duplicate records, inconsistencies, missing values, invalid data, and other data-quality issues. Support data lineage, metadata management, master data management, source-to-target mapping, and data-flow documentation. Contribute to data-quality improvement and modernization strategies. Spatial and geospatial analysis Analyze, integrate, and interpret spatial and non-spatial datasets. Apply geospatial analysis techniques to support planning, compliance, reporting, and decision-making. Manage spatial data and related geographic information. Identify patterns, relationships, trends, and insights across geographic and non-geographic data. Communicate spatial findings through maps, dashboards, reports, presentations, and other visualization tools. Collaborate with GIS specialists and information-management professionals to develop fit-for-purpose analytical solutions. Support the use of geospatial data in business intelligence, program analysis, and regulatory activities. Business intelligence and data visualization Develop business-intelligence solutions, dashboards, reports, and analytical products. Use tools such as Power BI, Tableau, R Shiny, ArcGIS, or comparable platforms. Present analytical findings through visualizations that are accessible and understandable to technical and nontechnical audiences. Develop reports and dashboards that support performance monitoring, compliance, planning, and management decisions. Ensure visualizations accurately represent data, assumptions, trends, relationships, and limitations. Support dashboard requirements, data definitions, measures, calculations, refresh processes, and data-quality controls. Research and analytical problem-solving Conduct research and investigate complex data, business, program, and regulatory issues. Apply statistical analysis, data mining, predictive analytics, artificial intelligence, machine learning, and data-modeling techniques. Assess complex datasets to identify data-quality, structural, completeness, validity, and consistency issues. Develop and document analytical frameworks, research methods, models, assumptions, and recommendations. Apply innovative analytical approaches, emerging technologies, and industry best practices. Identify risks, constraints, limitations, and dependencies affecting analytical outcomes. Recommend solutions based on evidence, data quality, feasibility, business value, and stakeholder needs. Collaboration and project support Collaborate with business stakeholders, information-management specialists, GIS specialists, business analysts, project teams, and technical resources. Participate in project planning, requirements gathering, implementation, testing, and reporting. Support project-management activities associated with data, analytics, modernization, migration, and information-management initiatives. Track analytical deliverables, milestones, dependencies, risks, and issues. Work effectively within multidisciplinary teams in a complex project environment. Support business and technical teams in understanding analytical requirements and findings. Contribute to continuous improvement of data, reporting, analytics, and information-management practices. Technical competencies Data science and predictive analytics. Statistical analysis, mathematics, and data mining. Machine learning and artificial intelligence. Predictive modeling and pattern recognition. Data modeling and analytical framework development. SQL for data access, extraction, transformation, and analysis. Python, R, or comparable data-analysis and modeling languages. Relational databases and database-management systems. Data warehousing and ETL processes. Data integration and data transformation. Data governance, metadata management, data lineage, and master data management. Data quality assessment and remediation. Spatial data management and geospatial analysis. Power BI. Tableau. R Shiny. ArcGIS. Git and source-control practices. Structured, semi-structured, unstructured, historical, and legacy datasets. Data migration, digitization, modernization, and records conversion. Technical documentation, data dictionaries, mapping documents, and data-flow diagrams. Information-management standards, accessibility requirements, and applicable GO-ITS standards. Required Qualifications And Experience Demonstrated knowledge of data analytics, including relational databases, geospatial analysis, spatial data management, data visualization, and analytical frameworks. Demonstrated proficiency with analytics and visualization tools such as Power BI, Tableau, R Shiny, ArcGIS, or equivalent platforms. Demonstrated ability to analyze, integrate, and interpret spatial and non-spatial datasets. Demonstrated ability to develop and apply predictive models, analytical frameworks, business-intelligence solutions, and geospatial-analysis techniques. Experience identifying business problems that can be addressed using data analysis. Experience identifying relevant data sources and collecting large structured and unstructured datasets. Experience with data mining, mathematics, statistical analysis, and predictive modeling. Extensive experience with pattern recognition and predictive analytics. Extensive experience with Python, R, or comparable programming languages. Experience working with large datasets and complex data environments. Experience with database-management systems. Experience with Git or other code-version-control systems. Experience developing data-driven solutions to improve business performance and decision-making. Experience collaborating with cross-functional teams to integrate data insights into business strategies. Project-management experience. Awareness of emerging information and information-technology trends. Excellent analytical, problem-solving, decision-making, communication, interpersonal, and negotiation skills. Ability to work effectively as part of a team and consistently meet deadlines. Technical knowledge and skills Data management and information governance — 50% Knowledge of information management, data management, database architecture, and data governance. Knowledge of metadata management, master data management, data lineage, ETL, data warehousing, and data integration. Knowledge of data modernization and digitization methodologies. Experience with records conversion and legacy-data management. Knowledge of structured, semi-structured, unstructured, historical, and legacy datasets. Proficiency in statistical analysis, data mining, predictive analytics, artificial intelligence, machine learning, research methodologies, and data modeling. Knowledge of relational databases, geospatial analysis, spatial data management, and data visualization. Proficiency with SQL across multiple platforms and repositories. Proficiency with Python, R, or equivalent tools for analysis, modeling, automation, and transformation. Proficiency with Power BI, Tableau, R Shiny, ArcGIS, or comparable platforms. Knowledge of information-management standards, data-governance frameworks, accessibility requirements, and applicable GO-ITS standards. Research, analytical, and problem-solving skills — 40% Ability to analyze complex datasets and identify duplicates, inconsistencies, missing values, invalid data, and other quality issues. Ability to support data migration, digitization, modernization, and information-management initiatives through analysis and quality assessment. Ability to identify, assess, and document business data requirements. Ability to translate business data requirements into analytical solutions. Ability to identify trends, patterns, relationships, and insights in large and complex datasets. Ability to analyze and interpret spatial and non-spatial information. Ability to develop and apply predictive models, analytical frameworks, business-intelligence solutions, and geospatial-analysis techniques. Ability to integrate multiple data sources and provide evidence-based recommendations. Ability to apply innovative analytical approaches, emerging technologies, and industry best practices. Communication and general skills — 10% Strong verbal and written communication skills. Ability to communicate complex analytical findings to technical and nontechnical audiences. Ability to prepare technical documentation, business reports, data dictionaries, source-to-target mapping documents, and data-flow diagrams. Ability to collaborate with business stakeholders, information-management specialists, GIS specialists, business analysts, project teams, and technical resources. Ability to communicate analytical and geospatial findings through reports, dashboards, maps, presentations, and other visualization tools. Ability to facilitate discussions and present recommendations. Ability to support informed decision-making. Ability to work effectively within multidisciplinary teams and complex project environments. Preferred Qualifications Advanced degree in Social Science, Statistics, Data Science, Mathematics, Computer Science, or a related discipline. Data Science Professional Certificate, including the IBM Data Science Professional Certificate. Google Data Engineer certification. Experience with government, public-sector, regulatory, compliance, or policy-related datasets. Experience with enterprise data modernization, digitization, and records-conversion initiatives. Experience with advanced geospatial analytics and GIS platforms. Experience supporting accessibility and information-management standards. Experience with data governance, metadata, lineage, master data, and data-quality programs. Documentation and communication deliverables Technical documentation. Business reports. Data dictionaries. Source-to-target mapping documents. Data-flow diagrams. Analytical frameworks and model documentation. Data-quality assessment results. Predictive-model outputs and evaluation findings. Business-intelligence dashboards and visual reports. Geospatial maps and analytical visualizations. Research findings and evidence-based recommendations. Data-migration and modernization analysis. Presentations for technical, business, and program stakeholders. Success in this role Success will require transforming complex spatial and non-spatial data into reliable insights, predictive models, visual products, and recommendations that support government planning, compliance, reporting, modernization, and decision-making. The ideal candidate will combine strong data-science expertise with practical experience in data management, geospatial analysis, visualization, governance, and stakeholder communication. Work arrangement and assignment details This is a fully onsite position in Toronto, Ontario. The estimated assignment period is October 26, 2026, through October 5, 2027. The assignment consists of 240 business days. An extension of 125 business days is available. No security clearance is required. The source JD does not specify salary, contract type, or working hours beyond 7.25 hours per day. Candidates should confirm employment and engagement details during the recruitment process. How to apply Interested candidates should submit the following documents to [email protected] or [email protected] by Tuesday, September 8, 2026, at 10:00 AM EST: Updated resume in Word format. Completed skills matrix. References. Expected hourly rate. Visa status. LinkedIn ID. Applications without the mandatory documents cannot be submitted. Candidates who are not interested are encouraged to forward this opportunity to qualified Senior Data Scientists, Predictive Analytics Specialists, Data Analysts, Business Intelligence Developers, Geospatial Data Scientists, or Data Modernization Consultants.

Develop predictive models, analytical frameworks, and geospatial insights to support government planning and decision-making. Manage complex datasets through integration, cleansing, and modernization while creating business intelligence dashboards for stakeholders.

Requires extensive experience in Python, R, and SQL, along with proficiency in geospatial tools like ArcGIS and visualization platforms like Power BI. Candidates must demonstrate expertise in statistical analysis, machine learning, and data governance within complex data environments.

• Visualisation de donnéesSouhaitée
• Power BISouhaitée
• SQLSouhaitée
• TableauSouhaitée
• Apprentissage automatiqueSouhaitée
• GitSouhaitée
• PythonSouhaitée

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Data Science
• Predictive Modeling
• Machine Learning
• Python
• R
• SQL
• Power BI
• Tableau
• ArcGIS
• Geospatial Analysis
• Statistical Analysis
• Data Governance
• ETL
• Data Visualization
• Git
• Data Mining

• Data & Analytics
• Government & Public Sector
• Technology
• Science & Research
• Consulting

Formation minimale

Diplôme professionnel

Expérience minimale

5+ ans

Postuler avant le

4 oct. 2026

Langue de l’offre

anglais

Heures de travail

37 heures par semaine

Niveau d’expérience

Entry level