Lead Data Scientist - Insights & Data Monetisation

at  ShopriteX

QUALIFICATIONS AND EXPERIENCE

• Bachelor’s Degree in Engineering, Data Science, Computer Science, Mathematics, Statistics, Information Technology, Information Systems, or a related field – (essential).
• Post Graduate Degree in the above or related fields – (desired).
• 6+ years’ experience in a similar role, consistently leading and solving complex business and technology problems through applying sophisticated data science and software engineering techniques within a fast-paced environment – (essential).
• Extensive experience in software development, particularly in managing the SDLC, version control, CI/CD, and modular codebase management – (essential).
• Expertise in SQL, Python, data science toolkits, and engineering practices – (essential).
• Experience working with optimization algorithms and packages such as ORTools and Optuna – (desired).
• Experience delivering project outcomes using design thinking, lean, and agile principles – (essential).
• Experience in a retail environment – (highly desired).

ROLE PURPOSE

The purpose of the Lead Data Scientist role is to lead the delivery of complex data science and engineering solutions that provide business value, driving data as a competitive advantage for Shoprite. This role oversees a portfolio of data science and engineering projects, working across a multidisciplinary, agile team to lead, guide, and provide technical expertise in solving a variety of use cases. The Lead Data Scientist for this role must possess a strong engineering bias, with a focus on model and software development, systems integration, and the management of end-to-end data science solutions that scale across the enterprise. Additionally, this role includes coaching and mentoring other data scientists, fostering the growth of engineering capabilities within the team to create a high-performing, technically proficient team.
This role offers one the opportunity to operate at the cutting edge of retail data science, and resides within the ShopriteX Data & Insights Monetisation team, focused on harnessing Shoprite’s wealth of customer and transactional data to unlock insights for our supplier community, provide intelligent assortment and ranging across thousands of stores, and competitively price products to ensure our customers get the best deals possible.

ROLE DESCRIPTION

• Contribute to the Development of ShopriteX’s Data Strategy: Collaborate on the development of a strategy that delivers best-in-class analytics, data science, and engineering solutions.
• Collaborate with Stakeholders: Work with business stakeholders, including senior leaders, data, and software teams, to identify business requirements, model and frame business scenarios, and deliver impactful solutions that optimize business processes and decision-making.
• Enable Deployment of Robust Solutions: Partner closely with data and software engineering teams to design, develop, and deploy fit-for-purpose, scalable solutions that integrate seamlessly into the company’s ecosystem.
• Manage SDLC and Project Prioritization: Lead the software development lifecycle (SDLC) for data science products, prioritizing and delegating roles and responsibilities, and making trade-offs between business and technical considerations.
• Develop Analytics and Engineering-Focused Products: Utilise machine learning, natural language processing, and advanced engineering principles to build and deploy products that deliver measurable business value.
• Oversee Model Implementation and Integrity: Guide the data science team in defining, implementing, and monitoring models, ensuring accuracy, integrity, and robustness.
• Ensure Data Quality and System Performance: Define data quality expectations and continuously monitor and track the performance of data systems, models, and their integration into broader systems.
• Advanced Data Preparation and Engineering: Conduct sophisticated data preparation and engineering to ensure data is reduced, shaped, and optimized for downstream processes.
• Apply Predictive and Descriptive Techniques: Utilize a comprehensive set of predictive and descriptive modeling techniques, ensuring the application is suited to achieving specific business objectives, including decision trees and association rules.
• Resolve Data and Engineering Challenges: Identify and address data and system engineering challenges, proposing and implementing appropriate solutions.
• Communicate and Present Analytical Findings: Present complex analytical findings and engineering results to senior stakeholders using data visualization techniques to tell compelling stories that align with enterprise goals.
• Evaluate Emerging Technologies: Stay current with new and emerging technologies, developing prototypes and proof of concepts that drive innovation.
• Serve as a Technical Leader and Mentor: Serve as a domain expert, sharing engineering and data science best practices with team members and cross-functional partners. Coach and mentor data scientists and engineers to foster professional development and technical expertise.
• Stay Updated on Engineering and Data Science Advances: Keep up with the latest research and developments in data science, software engineering, and adjacent fields to ensure cutting-edge techniques and methods are applied.