Data Scientist - Senior

Innosystech Inc; Toronto; On-site; CAD 90,000 - 130,000; Full time

Job summary

Innosystech Inc in Toronto is seeking a Senior Data Scientist to lead advanced analytics initiatives for a digital modernization project within Ontario's heritage programs. This onsite contract role focuses on integrating spatial and non-spatial data to drive decisions.

You will build predictive models, conduct geospatial analyses, and collaborate with GIS and business stakeholders to deliver dashboards and reports using SQL, Python, and BI tools.

Qualifications

• Demonstrates knowledge of data analytics, including relational databases, geospatial analysis, spatial data management, data visualization, and analytical frameworks used to support business decision-making.
• Demonstrates proficiency with analytics and visualization tools such as Power BI, Tableau, R Shiny, ArcGIS, or equivalent reporting and analytical platforms.
• Demonstrates the ability to analyze, integrate, and interpret both spatial and non-spatial datasets to support planning, compliance, reporting, and decision-making activities.
• Demonstrates the ability to develop and apply predictive models, analytical frameworks, business intelligence solutions, and geospatial analysis techniques.

Responsibilities

• Analyze complex data sets to generate predictive models and insights that inform business strategies
• Uses statistical methods, machine learning, and programming languages to interpret data and provide actionable insights
• Initiate, research, develop and manage new information studies and devise innovative statistical models for data analysis
• Conduct initial analyses to understand data's characteristics and identify potential patterns or trends

Skills

Data analytics

Geospatial analysis

SQL

Python/R

Tools

Power BI

Tableau

R Shiny

ArcGIS

Job description

Job Title: Data Scientist - Senior

Location: Toronto

Work Model: Onsite

Duration: 11 months

Extension: 1 Time

Must Haves

• Demonstrates knowledge of data analytics, including relational databases, geospatial analysis, spatial data management, data visualization, and analytical frameworks used to support business decision-making.
• Demonstrates proficiency with analytics and visualization tools such as Power BI, Tableau, R Shiny, ArcGIS, or equivalent reporting and analytical platforms.
• Demonstrates the ability to analyze, integrate, and interpret both spatial and non-spatial datasets to support planning, compliance, reporting, and decision-making activities.
• Demonstrates the ability to develop and apply predictive models, analytical frameworks, business intelligence solutions, and geospatial analysis techniques.

Description / Responsibilities / Skills

Description

• Ministry of Citizenship and Multiculturalism (MCM) is undertaking a digital modernization initiative to replace and enhance existing archaeology and heritage program capabilities
• The initiative will modernize digital services, data management, and business processes to improve service delivery, information access, and decision-making across Ontario's heritage and archaeology programs

Responsibilities

• Analyze complex data sets to generate predictive models and insights that inform business strategies
• Uses statistical methods, machine learning, and programming languages to interpret data and provide actionable insights
• Initiate, research, develop and manage new information studies and devise innovative statistical models for data analysis
• Conduct initial analyses to understand data's characteristics and identify potential patterns or trends

Experience & Skill Set Requirements

Technical Knowledge / Skills (50%)

• Demonstrates knowledge of information management, data management, database architecture, data governance, metadata management, master data management, data lineage, extract/transform/load (ETL) processes, data warehousing, and data integration.
• Demonstrates knowledge of data modernization, digitization methodologies, records conversion, data quality improvement practices, and the management of structured, semi-structured, unstructured, historical, and legacy datasets.
• Demonstrates proficiency in statistical analysis, data mining, predictive analytics, artificial intelligence, machine learning, research methodologies, and data modelling techniques.
• Demonstrates proficiency with Structured Query Language (SQL) for accessing, extracting, transforming, and analyzing data across multiple platforms and repositories.
• Demonstrates proficiency using Python, R, or other equivalent tools for data analysis, modelling, automation, and data transformation.
• Demonstrates knowledge of information management standards, data governance frameworks, accessibility requirements, and applicable GO-ITS standards.

Research, Analytical and Problem-Solving Skills (40%)

• Demonstrates the ability to analyze and assess complex datasets to identify data quality issues such as duplicate records, inconsistencies, missing values, and invalid data.
• Demonstrates the ability to support data migration, digitization, modernization, and information management initiatives through data analysis and quality assessment activities.
• Demonstrates the ability to identify, assess, and document business data requirements and translate them into analytical solutions.
• Demonstrates the ability to identify trends, patterns, relationships, and insights within large and complex datasets to support business, program, and regulatory objectives.
• Demonstrates the ability to integrate data from multiple sources and provide recommendations that support evidence-based decision-making.
• Demonstrates the ability to apply innovative analytical approaches, emerging technologies, and industry best practices to address complex business challenges.

Communication and General Skills (10%)

• Demonstrates strong verbal and written communication skills, including the ability to communicate complex analytical findings to technical and non-technical audiences.
• Demonstrates the ability to prepare technical documentation, business reports, data dictionaries, source-to-target mapping documents, and data flow diagrams.
• Demonstrates the ability to collaborate effectively with business stakeholders, information management specialists, GIS specialists, business analysts, project teams, and technical resources.
• Demonstrates the ability to communicate analytical and geospatial findings through reports, dashboards, maps, presentations, and other visualization tools.
• Demonstrates the ability to facilitate discussions, present recommendations, and support informed decision-making.
• Demonstrates the ability to work effectively within multi-disciplinary teams in a complex project environment.