Data Scientist - Senior

The Code Crackers Inc; Toronto; On-site; CAD 90,000 - 130,000; Full time

Job summary

CSC is seeking a data analyst/scientist to analyze complex data sets, generate predictive models, and provide insights that inform business strategies. This role requires strong skills in statistics, ML, and data programming.

Responsibilities include applying statistical methods, using Python or R, and creating visual insights with Power BI, while collaborating with cross-functional teams to drive data-driven decisions.

Qualifications

• Experience identifying business problems solvable with data analysis.
• Experience mining large structured and unstructured datasets for client needs.
• Familiarity with data visualization tools (Power BI).
• Proficient in Python or R for data analysis, modelling and automation.
• Strong foundation in statistics, data mining and ML techniques.
• Able to collaborate with cross-functional teams to drive data-driven decisions.

Responsibilities

• Analyze complex data sets to generate predictive models and insights that inform business strategies.
• Use 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 analysis

Power BI

Python

R

SQL

Machine learning

Data visualization

Statistical methods

Cross-functional collaboration

Big data

Git

Education

Advanced degree in statistics or related field

Tools

Git

SQL

Python libraries

Job description

“CSC may exercise its option to extend the SOW beyond October 5, 2027, provided that the Master Service Agreement is extended. Any such extension shall be on the same terms, conditions, and covenants as those contained in the SOW.”

Responsibilities

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

General Skills

1. 1. Experience identifying business problems that can be addressed using data analysis
2. 2. Experience identifying relevant data sources and sets to mine for client business needs, and collect large structured and unstructured datasets and variables
3. 3. Familiarity and experience with data visualization tools e.g. Power BI
4. 4. Experience and proficiency with data mining, mathematics and statistical analysis
5. 5. Experience developing data-driven solutions to improve business performance and decision making
6. 6. Experience collaborating with cross-functional teams to integrate data insights into business strategies
7. 7. Extensive experience in pattern recognition and predictive modeling
8. 8. Extensive experience with programming languages like Python or R
9. 9. Experience working with big data sets
10. 10. Experience with code version control systems such as GIT
11. 11. Experience with database management systems
12. 12. Project management experience
13. 13. Awareness of emerging I&IT trends and technologies
14. 14. Excellent analytical, problem-solving and decision-making skills; verbal and written communication skills; interpersonal and negotiation skills
15. 15. A team player with a track record for meeting deadlines

Desirable Skills

1. 16. Advanced degree in social science or statistics
2. 17. Certifications such as the Data Science Professional Certificate (offered by IBM) or Google Data Engineer certification

• Skills • Experience and Skill Set Requirements

Technical Knowledge / Skills (50%)

1. 1. 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.
2. 2. 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.
3. 3. Demonstrates proficiency in statistical analysis, data mining, predictive analytics, artificial intelligence, machine learning, research methodologies, and data modelling techniques.
4. 4. Demonstrates knowledge of data analytics, including relational databases, geospatial analysis, spatial data management, data visualization, and analytical frameworks used to support business decision-making.
5. 5. Demonstrates proficiency with Structured Query Language (SQL) for accessing, extracting, transforming, and analyzing data across multiple platforms and repositories.
6. 6. Demonstrates proficiency using Python, R, or other equivalent tools for data analysis, modelling, automation, and data transformation.
7. 7. Demonstrates proficiency with analytics and visualization tools such as Power BI, Tableau, R Shiny, ArcGIS, or equivalent reporting and analytical platforms.
8. 8. Demonstrates knowledge of information management standards, data governance frameworks, accessibility requirements, and applicable GO-ITS standards.

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

1. 1. Demonstrates the ability to analyze and assess complex datasets to identify data quality issues such as duplicate records, inconsistencies, missing values, and invalid data.
2. 2. Demonstrates the ability to support data migration, digitization, modernization, and information management initiatives through data analysis and quality assessment activities.
3. 3. Demonstrates the ability to identify, assess, and document business data requirements and translate them into analytical solutions.
4. 4. Demonstrates the ability to identify trends, patterns, relationships, and insights within large and complex datasets to support business, program, and regulatory objectives.
5. 5. Demonstrates the ability to analyze, integrate, and interpret both spatial and