Data Analyst

end-to-end data science work — from data gathering and preparation to advanced modelling and deployment — to deliver actionable insights and predictive capabilities.
 
You will collaborate closely with business stakeholders, data engineers, and analysts to design, implement, and optimize data-driven solutions. A strong background in statistical analysis, data mining, linear programming/optimization, and predictive modelling is required, along with proficiency in translating data into
strategic recommendations.

Advantages
ON SITE role - candidate is expected in the office 5 days a week, however there is some management discretion to work from home one day a week. But this is completely at management discretion. The intent is 5 days in office.

Responsibilities
Key Responsibilities
Data Analysis & Modelling
 Develop, implement, and maintain statistical and machine learning models to address business challenges.
 Conduct advanced statistical analyses, including regression, hypothesis testing, time-series forecasting, and multivariate analysis.
 Apply data mining and pattern recognition techniques to identify trends, anomalies, and actionable insights.
 Use linear programming and optimization methods to improve operational efficiency and decision-making.

Data Management & Preparation
 Collaborate with data engineering teams to ensure robust data pipelines and efficient data structures.
 Source, clean, and validate data from multiple internal and external sources.
 Ensure the integrity, accuracy, and quality of datasets used for modelling.

Business Collaboration & Communication
 Partner with stakeholders to understand business needs and translate them into analytical projects.
 Present results and recommendations clearly and concisely to technical and non-technical audiences.

Innovation & Continuous Improvement
 Research and apply new methodologies, tools, and technologies to enhance modelling capabilities.
 Stay current on advancements in data science, AI/ML frameworks, and best practices.
 Drive continuous improvement in processes, tools, and analytical frameworks.


Qualifications
Experience
 3–5 years of professional data science experience, including analytics, model development, and deployment in a business environment.
 Proven track record in predictive modelling, optimization, and statistical analysis.
Technical Skills
 Strong proficiency in Python or R for statistical analysis and modelling.
 Solid knowledge of SQL for data querying and manipulation.
 Experience with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and statistical packages.
 Familiarity with optimization tools (e.g., PuLP, Gurobi, CPLEX) is highly desirable.
 Proficiency in data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn).
 Strong understanding of relational databases and big data platforms (e.g., Spark, Hadoop) is a plus.
Soft Skills
 Exceptional problem-solving, critical thinking, and analytical skills.
 Strong communication skills, with the ability to explain complex concepts to diverse audiences.
 Collaborative team player who thrives in a fast-paced environment.
Key Competencies
 Analytical Rigour: Ability to break down complex problems into structured analytical frameworks.
 Business Acumen: Understanding how to connect data insights to strategic business objectives.
 Innovation Mindset: Continually seeking new approaches to improve data-driven decision-making.
 Execution Excellence: Ability to manage multiple priorities and deliver high-quality results on time.

Summary
Reporting to the Vice President of IT, the Senior Data Analyst will serve as a highly skilled and motivated member of our team. The ideal candidate will bring 3–5 years of professional experience applying analytics, statistical modelling, and advanced data techniques to solve complex business problems. This role involves
end-to-end data science work — from data gathering and preparation to advanced modelling and deployment — to deliver actionable insights and predictive capabilities.
 
You will collaborate closely with business stakeholders, data engineers, and analysts to design, implement, and optimize data-driven solutions. A strong background in statistical analysis, data mining, linear programming/optimization, and predictive modelling is required, along with proficiency in translating data into
strategic recommendations.

Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.

Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.