Consultant - Retail Specialized Data Scientist

Role Overview: As a Retail Specialized Data Scientist at Level 9 in the SnC GN Data & AI team, you will be responsible for utilizing advanced analytics, machine learning, and statistical modeling techniques to support data-driven decision-making in the retail sector. Your role will involve collaborating with various teams such as marketing, product management, supply chain, and customer insights to drive business strategies and innovations. The ideal candidate should possess experience in retail analytics and demonstrate the ability to transform data into actionable insights. Key Responsibilities: - Leverage Retail Knowledge: Utilize your in-depth understanding of the retail industry, including merchandising, customer behavior, and product lifecycle, to design AI solutions that cater to crucial retail business requirements. - Gather and clean data from diverse retail sources like sales transactions, customer interactions, inventory management, website traffic, and marketing campaigns. - Apply machine learning algorithms such as classification, clustering, regression, and deep learning to improve predictive models. - Utilize AI-driven techniques for personalization, demand forecasting, and fraud detection. - Implement advanced statistical methods to optimize existing use cases and develop new products to address emerging challenges and use cases. - Stay abreast of the latest trends in data science and retail technology. - Collaborate effectively with executives, product managers, and marketing teams to translate insights into actionable business strategies. Qualifications Required: - A solid understanding of retail industry dynamics, encompassing key performance indicators (KPIs) like sales trends, customer segmentation, inventory turnover, and promotions. - Strong communication skills to convey complex data insights to non-technical stakeholders, including senior management, marketing, and operational teams. - Attention to detail to ensure data quality, accuracy, and consistency while handling large, complex datasets. - Proficiency in Python for data manipulation, statistical analysis, and machine learning (libraries like Pandas, NumPy, Scikit-learn). - Expertise in supervised and unsupervised learning algorithms. - Minimum 3 years of experience in the field. - Educational background in Data Science, Statistics, Computer Science, Mathematics, or related disciplines. Note: The job description does not include any additional details about the company.,