Data Scientist – Senior

• Analyze complex structured, semi-structured, unstructured, historical, and legacy datasets to identify patterns, trends, relationships, and actionable insights.
  
• Develop predictive models, statistical models, machine learning solutions, and analytical frameworks to support business strategies and decision-making.
  
• Conduct research and initiate innovative information studies and statistical analysis initiatives.
  
• Identify business problems that can be addressed through data analytics and translate business requirements into analytical solutions.
  
• Identify, collect, integrate, and analyze relevant data sources and large datasets.
  
• Perform data mining, statistical analysis, pattern recognition, and predictive analytics.
  
• Analyze data quality issues, including duplicate records, inconsistencies, missing values, and invalid data.
  
• Support data migration, digitization, modernization, records conversion, and information management initiatives.
  
• Integrate and interpret data from multiple platforms and repositories.
  
• Analyze and integrate both spatial and non-spatial datasets to support planning, compliance, reporting, and evidence-based decision-making.
  
• Develop and apply geospatial analysis techniques and spatial data management practices.
  
• Build dashboards, reports, visualizations, maps, and analytical products for technical and non-technical stakeholders.
  
• Develop data-driven solutions to improve business performance and organizational decision-making.
  
• Collaborate with business stakeholders, information management specialists, GIS specialists, business analysts, project teams, and technical resources.
  
• Prepare technical documentation, data dictionaries, source-to-target mapping documents, data flow diagrams, and business reports.
  
• Apply emerging technologies, innovative analytical approaches, and industry best practices to complex business challenges.
  
• Support and contribute to data governance, metadata management, master data management, and data lineage initiatives.
  

Requirements

• Strong knowledge of information management and data management principles.
  
• Experience with database architecture, database management systems, relational databases, and data integration.
  
• Knowledge of data governance, metadata management, master data management, and data lineage.
  
• Experience with ETL processes, data warehousing, and data integration methodologies.
  
• Experience managing and analyzing structured, semi-structured, unstructured, historical, and legacy datasets.
  
• Knowledge of data modernization, digitization methodologies, records conversion, and data quality improvement practices.
  

• Strong proficiency in statistical analysis, mathematics, data mining, and predictive analytics.
  
• Extensive experience with artificial intelligence and machine learning techniques.
  
• Experience developing predictive models and analytical frameworks.
  
• Strong understanding of research methodologies and data modelling techniques.
  
• Extensive experience in pattern recognition and identifying trends and relationships within complex datasets.
  

• Strong proficiency in Structured Query Language (SQL) for accessing, extracting, transforming, and analyzing data across multiple platforms and repositories.
  
• Strong hands-on experience with Python and/or R for data analysis, modelling, automation, and data transformation.
  
• Experience working with large and complex datasets.
  
• Experience using code version control systems such as Git.
  

• Power BI
  
• Tableau
  
• R Shiny
  
• ArcGIS
  
• Or equivalent reporting, visualization, and analytical platforms
  

• Demonstrated knowledge of geospatial analysis and spatial data management.
  
• Experience analyzing relational databases and spatial datasets.
  
• Ability to analyze, integrate, and interpret both spatial and non-spatial datasets.
  
• Experience applying geospatial analysis techniques to support planning, compliance, reporting, and decision-making.
  
• Ability to communicate geospatial findings through maps, dashboards, reports, presentations, and visualization tools.
  

• Ability to assess complex datasets and identify data quality issues.
  
• Experience documenting business data requirements and translating them into analytical solutions.
  
• Ability to identify trends, patterns, relationships, and insights within large datasets.
  
• Experience integrating data from multiple sources and repositories.
  
• Ability to develop and apply predictive models, business intelligence solutions, and geospatial analytical techniques.
  
• Strong ability to provide recommendations supporting evidence-based decision-making.
  
• Ability to apply innovative analytical approaches and emerging technologies to solve complex business problems.
  

• Excellent verbal and written communication skills.
  
• Ability to communicate complex analytical findings to both technical and non-technical audiences.
  
• Experience preparing technical documentation and analytical reports.
  
• Experience creating data dictionaries, source-to-target mapping documents, and data flow diagrams.
  
• Ability to present analytical findings through dashboards, reports, maps, and presentations.
  
• Strong stakeholder management, interpersonal, and negotiation skills.
  
• Ability to facilitate discussions and present recommendations to support informed decision-making.
  
• Proven ability to work effectively within multidisciplinary teams and complex project environments.
  
• Strong project management and organizational skills with a proven ability to meet deadlines.
  

• Knowledge of information management standards and data governance frameworks.
  
• Awareness of accessibility requirements and applicable GO-ITS standards.
  
• Understanding of emerging I&IT trends and technologies.
  

• Advanced degree in Statistics, Data Science, Social Sciences, Computer Science, Analytics, or a related discipline.
  
• Data science or analytics certifications such as:
  
• IBM Data Science Professional Certificate
  
• Google Data Engineer Certification
  
• Other relevant data science, analytics, AI, or machine learning certifications
  

• Data analytics and relational databases
  
• Geospatial analysis
  
• Spatial data management
  
• Data visualization and analytical frameworks
  
• Power BI, Tableau, R Shiny, ArcGIS, or equivalent analytics platforms
  
• Analysis, integration, and interpretation of both spatial and non-spatial datasets
  
• Predictive modelling and analytical frameworks
  
• Business intelligence solutions
  
• Geospatial analysis techniques
  
• SQL
  
• Python and/or R
  
• Statistical analysis and data mining
  
• Machine learning and AI
  
• Large and complex datasets
  
• Data integration and ETL
  
• Data quality assessment
  
• Data governance and information management
  
• Excellent communication and stakeholder collaboration skills