Sr. Content Designer

📍 Job Overview

Job Title: Sr. Content Designer

Company: Rogers Communications

Location: Edmonton, AB, CA; Winnipeg, MB, CA; Calgary, AB, CA; Vancouver, BC, CA; Fredericton, NB, CA; Halifax, NS, CA; Brampton, ON, CA; Moncton, NB, CA; Toronto, ON, CA

Job Type: Full-time

Category: Technology & Information Technology (Data Science/Analytics Focus)

Date Posted: July 22, 2026

Experience Level: 10+ Years

Remote Status: On-site

🚀 Role Summary

• This Sr. Content Designer role is deeply embedded within Rogers Communications' Technology team, focusing on advanced data science and analytics.

• The position requires a strategic approach to developing and deploying machine learning models and robust data pipelines for predictive analytics and process automation.

• Responsibilities include acting as a subject matter expert in data technologies, providing technical guidance, and translating complex data insights into actionable intelligence for executive audiences.

• The role emphasizes collaboration with marketing, product, UX, and digital analytics teams to ensure user-centered content design for digital platforms, aligning with brand strategy and measurable business outcomes.

📝 Enhancement Note: While the title is "Sr. Content Designer," the core responsibilities and required skills heavily indicate a Data Scientist or Senior Data Engineer role with a strong emphasis on translating technical findings into clear communications. The "content" aspect appears to relate to the output of data analysis and model performance, rather than traditional marketing or UX content.

📈 Primary Responsibilities

• Serve as a subject matter expert in data science and analytics, providing technical leadership in areas such as data mining, hypothesis testing, predictive modeling, and machine learning.

• Construct and maintain scalable, production-grade data ingestion and model consumption pipelines, integrating CI/CD practices for continuous improvement.

• Develop, test, and deploy machine learning models to enhance predictive analytics and automate business processes, continuously monitoring performance for accuracy and efficiency.

• Conduct rigorous statistical analysis and hypothesis testing to validate model robustness and effectiveness.

• Support data analytics and reporting requests for Care Operations, generating detailed, systematic, and efficient insights to inform organizational strategy, including executive-level decision-making.

• Consult with business stakeholders to identify needs, strategic gaps, and prioritize resources for optimal solution delivery.

• Champion industry best practices in data science and data engineering, fostering a collaborative culture and establishing cross-functional team processes.

• Collaborate with leaders in big data and AI to integrate cutting-edge capabilities and best practices into Rogers' operations.

• Stay abreast of the latest analytics technologies, applying them creatively to maximize business impact.

📝 Enhancement Note: The responsibilities listed are highly technical and align with a senior Data Scientist or Data Engineering role. The emphasis on "content" is interpreted as the communication of data insights and model outputs, not traditional content creation.

🎓 Skills & Qualifications

Education:

• Graduate degree (Masters or above preferred) in quantitative sciences (e.g., Statistics, Computer Science, Mathematics, Engineering). Experience:

• Minimum of 8 years of progressive experience in advanced data science, with a comprehensive understanding of statistical methodologies, data mining, and modeling techniques.

• Proven experience in building, deploying, and maintaining production-grade machine learning models and data pipelines in cloud environments. Required Skills:

• Proficiency in programming languages such as R and Python, along with relevant data science toolkits (e.g., dplyr, Scipy).

• Strong understanding of machine learning frameworks like Keras, PyTorch, and Tensorflow.

• Hands-on experience with cloud platforms, specifically AWS and/or Azure.

• Experience with big data technologies such as Hadoop, Spark, and Dask.

• Familiarity with SQL databases and NoSQL databases (e.g., MongoDB, ElasticSearch).

• Experience with version control and CI/CD tools (e.g., bash shell, Git, Jenkins).

• Experience in production deployment and maintenance of data movement pipelines and analytical ML models.

• Familiarity with Agile, SCRUM, or Lean methodologies, particularly in implementing DevOps practices.

• Strong analytical and problem-solving skills with resourcefulness in troubleshooting complex issues.

• Excellent organizational skills, with the ability to manage multiple projects effectively in a fast-paced, high-pressure environment.

• Rigorous planning ability coupled with flexibility and adaptability to changing business requirements.

• Superior communication and presentation skills, with the ability to distill complex technical concepts into clear, concise information for diverse audiences.

• Strong interpersonal skills to build rapport and foster strong stakeholder relationships.

• A passion for new technologies, ambition, and a willingness to share innovative ideas. Preferred Skills:

• Experience in data mining, hypothesis testing, and predictive modeling.

• Familiarity with DevOps implementation.

📝 Enhancement Note: The "Sr. Content Designer" title is a significant misnomer given the extensive technical requirements. The emphasis on data science, machine learning, cloud technologies, and big data tools clearly points to a senior technical role within the data and analytics domain. The "content" aspect is likely related to reporting, documentation, and communication of technical findings.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrations of successfully developed and deployed machine learning models, showcasing the entire lifecycle from data ingestion to model deployment and monitoring.

• Case studies detailing the creation and optimization of robust, scalable data pipelines for data ingestion and model consumption, highlighting CI/CD integration.

• Examples of statistical analysis and hypothesis testing applied to validate model performance and ensure robustness.

• Documentation or examples of translating complex data points into concise, actionable insights for executive-level consumption, demonstrating effective data storytelling.

• Evidence of experience in supporting data analytics and reporting requests, particularly within operational contexts (e.g., Care Operations). Process Documentation:

• Workflow designs and optimization strategies for data pipelines and machine learning model development processes.

• Documentation of implementation and automation methods used for data ingestion, model deployment, and CI/CD integration.

• Metrics and performance analysis reports demonstrating the impact and efficiency of developed models and pipelines.

📝 Enhancement Note: Given the technical nature of the role, a portfolio should focus on tangible technical outputs, project execution, and the ability to communicate complex technical results. This goes beyond typical content design portfolios and leans towards a data science or engineering project portfolio.

💵 Compensation & Benefits

Salary Range:

• Based on industry benchmarks for Sr. Data Scientists/Senior Data Engineers in major Canadian metropolitan areas (Toronto, Vancouver, Calgary, Edmonton) with 10+ years of experience, a competitive salary range is estimated between CAD $130,000 - $180,000 annually. This estimate accounts for the specialized skills, extensive experience, and the complex nature of the role within a large telecommunications organization. Benefits:

• Competitive salary and annual bonus.

• Comprehensive and flexible health and dental benefits.

• Pension plan, RRSP, TFSA, and stock matching programs.

• Discounts on Rogers Services (up to 50%), Blue Jays Tickets (up to 50%), TSC items (25%), and wireless accessories (20%).

• Paid time off for volunteering.

• Company matching contributions to charities.

• Career development opportunities through "My Path" and priority for internal roles via "Rogers First."

• Robust wellness programs including Homewood Employee & Family Assistance, CBT, and virtual therapy sessions.

• Low or no-cost fitness membership. Working Hours:

• Full-time, typically 40 hours per week.

• Standard day shifts, with potential for flexibility depending on project needs and team collaboration requirements, though the role is primarily on-site.

📝 Enhancement Note: Salary estimation is based on publicly available data for senior data science roles in Canada, considering the specified experience level and the company's industry. The benefits package is extensive and well-detailed in the original posting.

🎯 Team & Company Context

🏢 Company Culture

Industry: Telecommunications and Media. Rogers Communications is a major player in the Canadian market, offering a wide range of services including wireless, internet, television, and home security. This industry context implies a need for robust data management and analytics to understand customer behavior, optimize service delivery, and drive innovation.

Company Size: Large Enterprise (likely 10,000+ employees, based on typical telecommunications company structures). This size suggests a complex organizational structure with numerous departments and opportunities for cross-functional collaboration.

Founded: Rogers Communications was founded in 1960. This long history indicates a stable company with established processes and a deep understanding of the Canadian market.

Team Structure:

• The role reports to the Senior Manager, Digital Content, within the Technology team.

• It involves close collaboration with marketing, product, UX, and digital analytics teams, indicating a cross-functional operational model.

• The team likely comprises specialists in various areas of data science, data engineering, and potentially some roles focused on translating technical findings into accessible formats. Methodology:

• Data-driven decision-making is a core aspect, utilizing advanced analytics, machine learning, and hypothesis testing.

• Emphasis on agile/SCRUM/lean methodologies for implementing DevOps and managing projects.

• A culture that promotes sharing of best practices, continuous learning, and innovation in data science and AI.

Company Website: https://jobs.rogers.com/

📝 Enhancement Note: The "Digital Content" reporting structure is unusual for a Data Scientist role. This suggests the team may be focused on the content of data insights and how they are presented to support digital platforms and customer journeys, rather than traditional editorial content.

📈 Career & Growth Analysis

Operations Career Level: This is a Senior-level position (10+ years of experience) within the Data Science/Analytics domain. It signifies a high degree of technical expertise, problem-solving capability, and the ability to provide guidance and mentorship. The role is expected to contribute significantly to strategic initiatives through advanced analytical insights.

Reporting Structure: The role reports to a Senior Manager, likely within a broader Technology or Digital division. This position offers significant autonomy in technical execution but requires alignment with management objectives and cross-functional team needs.

Operations Impact: The operations impact is substantial, as the role directly influences strategic decision-making through predictive analytics and actionable insights derived from complex data. By optimizing processes and automating tasks with machine learning, the role contributes to operational efficiency, cost savings, and enhanced customer experience across digital platforms.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific machine learning areas, big data technologies, or cloud data services.

• Leadership Development: Potential to move into management roles (e.g., Data Science Manager, Lead Data Scientist), guiding a team of analysts and engineers.

• Strategic Contribution: Influence the direction of data strategy and analytics initiatives across Rogers Communications.

• Cross-functional Exposure: Gain broader business understanding by working closely with marketing, product, and UX teams.

• Mentorship: Opportunity to mentor junior data scientists and engineers.

📝 Enhancement Note: The career progression is geared towards senior technical leadership or management within the data and analytics field, rather than traditional "content design" career paths.

🌐 Work Environment

Office Type: Primarily an on-site work environment, as explicitly stated for "Corporate Employees." This suggests a traditional office setting designed for in-person collaboration.

Office Location(s): Multiple locations across Canada are available for this role: Calgary (AB), Edmonton (AB), Vancouver (BC), Winnipeg (MB), Fredericton (NB), Moncton (NB), Halifax (NS), Brampton (ON), and Toronto (ON). The specific work location will be assigned.

Workspace Context:

• The workspace is expected to be collaborative, fostering interaction with colleagues from various departments (marketing, product, UX, digital analytics).

• Access to advanced technology and tools will be crucial for data analysis, model development, and pipeline management.

• Opportunities for direct engagement with team members and stakeholders to drive projects forward and share insights.

Work Schedule: Full-time, day shifts. While the role is on-site, the emphasis on "rigorous planning but also flexible and adaptable" suggests that some level of work-life balance is supported, within the constraints of an on-site requirement.

📝 Enhancement Note: The "Corporate Employees are expected to work onsite" statement is a strong indicator of the company's stance on remote work for this type of role, emphasizing in-person collaboration.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Application review focusing on technical qualifications, experience in data science, machine learning, and cloud technologies.

• Technical Assessment: Likely includes coding challenges (Python/R), case studies involving data analysis or model building, and in-depth discussions on statistical methodologies and ML concepts.

• Behavioral Interviews: Assessment of problem-solving skills, collaboration style, communication abilities, and cultural fit, with a focus on how the candidate handles complex challenges and works with stakeholders.

• Manager/Team Interviews: Deeper dives into specific project experience, technical approach, and alignment with team and company values.

Portfolio Review Tips:

• Technical Depth: Showcase projects with clear technical details – algorithms used, data handled, challenges overcome, and specific outcomes achieved.

• Quantifiable Results: Present metrics and KPIs that demonstrate the impact of your work (e.g., accuracy improvements, efficiency gains, cost savings, revenue impact).

• Process Documentation: Clearly outline the methodology used for each project, from data acquisition and cleaning to model selection, training, validation, and deployment.

• Problem-Solving Focus: Highlight complex problems solved and the innovative approaches taken.

• Communication Clarity: Ensure any code, visualizations, or explanations are clear, concise, and easy for both technical and non-technical audiences to understand. Be prepared to walk through your portfolio items step-by-step.

Challenge Preparation:

• Coding Proficiency: Practice Python/R coding exercises, focusing on data manipulation (Pandas, dplyr), statistical analysis, and ML model implementation.

• Case Study Scenarios: Prepare for case studies that require you to analyze a dataset, identify patterns, build a predictive model, or propose a data-driven solution to a business problem.

• Technical Explanations: Be ready to articulate complex technical concepts (e.g., gradient boosting, neural networks, cloud data services) clearly and concisely.

• Stakeholder Communication: Practice explaining technical findings and recommendations to a non-technical audience, focusing on business value and actionable insights.

📝 Enhancement Note: The "Content Designer" title suggests a potential focus on how technical findings are communicated. Therefore, portfolio presentations should emphasize not just the technical execution but also the clarity and impact of the insights presented.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: R, Python (essential for data analysis, modeling, scripting).

• Data Science Libraries: Scipy, dplyr, Pandas, NumPy, Scikit-learn, Keras, PyTorch, Tensorflow.

• Cloud Platforms: AWS (e.g., S3, EC2, SageMaker), Azure (e.g., Blob Storage, VMs, Azure ML).

• Big Data Technologies: Hadoop, Spark, Dask (for processing large datasets).

• Databases: SQL databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, ElasticSearch).

Analytics & Reporting:

• Tools for statistical analysis, hypothesis testing, and building dashboards (specific tools not listed, but likely includes Tableau, Power BI, or custom Python/R visualization libraries).

• Data mining tools and techniques. CRM & Automation:

• While not explicitly listed, understanding of how data science models integrate with CRM systems (e.g., Salesforce) and automation platforms for operationalizing insights would be beneficial.

• Familiarity with CI/CD tools like Git and Jenkins is crucial for pipeline automation and deployment.

📝 Enhancement Note: The tech stack is heavily geared towards data science and big data engineering, reinforcing the interpretation of this role as a senior technical position rather than a traditional content designer.

👥 Team Culture & Values

Operations Values:

• Data-Driven Decision Making: A core value likely emphasizing the use of data and analytics to inform all strategic and operational decisions.

• Innovation & Continuous Learning: Encouraging the exploration of new technologies and methodologies in data science and AI to drive business value.

• Collaboration & Cross-Functional Partnership: Fostering strong working relationships with teams across marketing, product, and UX to achieve common goals.

• Efficiency & Optimization: A focus on building scalable, efficient processes and models that deliver measurable business impact.

• Customer Centricity: Using data insights to understand and improve customer journeys and experiences.

Collaboration Style:

• Highly collaborative, requiring close interaction with diverse teams to gather requirements, integrate insights, and deploy solutions.

• Emphasis on clear communication of complex technical concepts to non-technical stakeholders.

• A culture that encourages sharing of knowledge, best practices, and constructive feedback to foster continuous improvement.

📝 Enhancement Note: The values are typical for a technology-driven organization focused on data and innovation. The "content" aspect likely ties into how these values are communicated and supported through data-derived insights.

⚡ Challenges & Growth Opportunities

Challenges:

• Translating Technical Complexity: Effectively communicating sophisticated data science concepts and model outputs to non-technical stakeholders across various departments.

• Data Integration & Quality: Working with potentially disparate and complex data sources to ensure data integrity and reliability for analysis.

• Rapid Technological Evolution: Keeping pace with the fast-changing landscape of AI, machine learning, and big data technologies.

• Balancing Innovation with Production: Ensuring that cutting-edge research and development are balanced with the need for stable, production-grade systems.

• On-site Requirement: Adapting to a fully on-site work environment after potentially having experience with remote or hybrid setups.

Learning & Development Opportunities:

• Advanced Training: Access to specialized courses and certifications in cloud technologies, AI/ML, and big data.

• Industry Conferences: Opportunities to attend leading data science and AI conferences to stay current with trends.

• Mentorship: Guidance from senior leaders and peers within Rogers' extensive technology division.

• Cross-functional Projects: Exposure to diverse business challenges and opportunities to apply data science skills in new contexts.

• Leadership Track: Development programs aimed at preparing individuals for leadership roles within the data and analytics field.

📝 Enhancement Note: The challenges are common for senior data professionals, with a specific note on the on-site work requirement. Growth opportunities are robust and typical for a large enterprise in the tech sector.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex machine learning project you led from conception to production. What were the key challenges, your technical approach, and the business impact?" (Focus on methodology, problem-solving, and ROI).

• "How do you ensure the robustness and reliability of your data pipelines and machine learning models in a production environment? Discuss your experience with CI/CD and monitoring." (Focus on engineering rigor and operational excellence).

• "Imagine you need to explain the results of a predictive model to a non-technical executive. How would you structure your presentation to ensure clarity and drive action?" (Focus on communication and stakeholder management). Company & Culture Questions:

• "Why are you interested in Rogers Communications, and how do you see your data science expertise contributing to our mission of connecting Canadians?" (Research Rogers' business, values, and recent initiatives).

• "How would you foster collaboration between data science and other departments like marketing or product development?" (Discuss your cross-functional collaboration style and experience).

• "Given our focus on innovation, how do you stay current with emerging trends in AI and machine learning, and how would you introduce new ideas within Rogers?" (Demonstrate your passion for learning and innovation). Portfolio Presentation Strategy:

• Structure: Organize your portfolio by project type or impact area. For each project, clearly state the problem, your role, the data used, the methodology/tools, the results (quantified), and lessons learned.

• Technical Deep Dive: Be prepared to walk through code snippets or architectural diagrams for key projects, explaining your technical decisions.

• Business Value Focus: For each project, articulate the business value or ROI achieved. Connect your technical work directly to business outcomes.

• Interactive Elements: If possible, prepare interactive dashboards or visualizations that showcase your analytical capabilities.

• Storytelling: Frame your projects as compelling stories that highlight your problem-solving journey and impact.

📝 Enhancement Note: Interview preparation should heavily emphasize technical depth, communication of complex results, and demonstrated impact, aligning with the senior data science/engineering expectations.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Rogers Careers portal via the provided job URL.

• Resume Optimization: Tailor your resume to highlight your 10+ years of experience in data science, machine learning, cloud technologies, and big data. Use keywords from the job description, such as "predictive modeling," "Python," "AWS," "Spark," and "CI/CD." Quantify your achievements with metrics whenever possible.

• Portfolio Preparation: Curate a portfolio that showcases your most impactful data science projects, including case studies on model development, data pipeline engineering, and statistical analysis. Be ready to present and discuss these in detail, focusing on technical execution and business impact.

• Technical Skill Refresh: Review core concepts in statistics, machine learning algorithms, data mining, and your proficiency in R, Python, SQL, and cloud platforms (AWS/Azure). Practice coding challenges.

• Company Research: Familiarize yourself with Rogers Communications' business, services, technology initiatives, and company values, particularly their commitment to innovation and customer connection.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

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