Data and Analytics Manager

at  Ackermans

QUALIFICATIONS:

Essential:

• Tertiary Degree in mathematics / statistics / information technology or related discipline

Preferred:

• Honours Degree in Data Science/ Computer Science/Information Systems / Statistics/Applied Mathematics / Business Administration / Economics / Operations Analysis / Operations Research

EXPERIENCE:

• At least 10 years’ working experience as a Data Analytics, Data Management or Data Insights position within the retail industry.
• Proven and successful experience in execution of data analytics initiatives.
• Setting up data architecture and design.
• Leading large analytical teams.
• Building and leading quantitative analyses.
• working with a wide range of data to deliver and deploy descriptive, predictive, and prescriptive models.

KNOWLEDGE & SKILLS:

Technical

• Proficient in data analysis tools (e.g. SQL, Python, R)
• Knowledge of cloud infrastructures for analytics.
• Experience in data visualization tools (e.g. Power BI)
• Knowledge of data warehousing for analytics.
• Problem solving using advanced analytics techniques.
• Highly proficient in working with data technologies.
• Experience in predictive modelling and predictive modelling way of thinking.

Strategic

• Strong business / commercial acumen.
• Deep understanding of a retail environment.
• Strategic thinking - Be a thought leader in the data and analytics space as an enabler to drive execution of strategy.
• Innovation mindset: Encourage facilitate a culture of innovation and continuous improvement.
• Scalability

Collaboration

• Excellent Communication - Interact on all levels/with numerous departments’ personnel and departmental heads / executive management.
• Good presentation skills and ability to simplify complex scenarios Data and information management.
• Reporting visualization.

Soft skills

• Resilience and ability to work under immense pressure
• Ability to communicate complicated technical concepts to non-technical stakeholders
• Building strong relationships with data stake holders and business stake holders
• Critical thinking
• Adaptability

Team Lead

• Motivational leader
• Team building
• Project management skills – resource management, planning, prioritization and execution
• Change management

• Enablement of Self-Help Data Analytics: Establish processes, technology, tools, skills, and knowledge for data management and consumption.
• Strategic Analytics Services: Define complex business questions, develop appropriate analytics models, and identify relevant data sources for analysis.
• Engagements: Form meaningful partnerships with business stakeholders to extract maximum value from data and analytics tools.
• Central Analytical Services: Ensure congruence of information in daily, weekly, and monthly reports and dashboards published across the business.
• Commercialization of Data: Support Business Units with robust data science capabilities to drive business value through advanced data, mathematical, and statistical models.
• Team Leadership and Training: Lead and train a team of analysts.
• Protection of Personal Information: Ensure compliance with POPIA for customers, employees, and suppliers.