Analytical Buyer
We are seeking a skilled and innovative Data Scientist to join our basketball specialty retail team. As a Data Scientist, you will play a crucial role in leveraging data analytics to optimize various aspects of our retail operations, enhance customer experience, and contribute to data-driven decision-making within the basketball retail domain. This role is ideal for a candidate with a passion for both data science and basketball. You will be working closely with the BounceWear Operations Managers & Brand Director.Half-time and/or freelance possible
Responsibilities:
Data Analysis and Interpretation:
- Analyse large datasets related to customer behaviour (generating from Google, Cross Point and other external data-points), sales, inventory, and marketing to derive actionable insights.
Customer Segmentation:
- Utilize clustering algorithms and customer segmentation techniques to identify and target specific customer groups for personalized marketing and product recommendations.
Demand Forecasting:
- Develop predictive models to forecast product demand, enabling effective inventory management and optimizing stock levels for popular basketball products.
- Support in optimizing the stock by providing guidelines in terms of Store Pick-up and Store Delivery from and to the warehouse.
- Support in optimizing the collections to be purchased per season in terms of quantities, colorways, product assortment, etc.
- Move Slow & Fast movers around to optimize overall BounceWear Sales performance.
Pricing Optimization:
- Collaborate with the pricing team to develop pricing models that align with market trends, competitor pricing, and customer preferences.
Basketball Trends Analysis:
- Stay abreast of basketball industry trends and integrate relevant data to guide product offerings, promotions, and marketing strategies.
User Experience Enhancement:
- Work with UX/UI teams to incorporate data insights into website and mobile app improvements, ensuring an optimal online shopping experience for basketball enthusiasts.
Recommendation Systems:
Implement recommendation algorithms to suggest relevant basketball products to customers based on their browsing and purchasing history.
A/B Testing:
- Design and conduct A/B tests to evaluate the impact of changes in marketing strategies, website features, or product offerings.
Collaboration with Cross-Functional Teams:
- Collaborate with marketing, sales, and inventory teams to align data-driven strategies and enhance overall business performance.
Qualifications:
- Advanced degree in Data Science, Statistics, Computer Science, or a related field.
- Proven experience applying data science techniques in a retail or e-commerce environment.
- Proficiency in programming languages such as Python or R and especially PowerBi
- Strong analytical and problem-solving skills.