Key Responsibilities: 1. Data Extraction, Cleaning, and Processing: Extract data from multiple sources (POS, ERP, and other systems), clean and transform it into usable formats for analysis. Ensure data integrity and consistency across all data systems, adhering to data governance standards. 2. Descriptive Analytics & Reporting: Analyze business data, generate reports, and create visualizations to provide insights into operational performance, sales, and inventory management. Develop interactive dashboards and automate regular reports for key stakeholders (e.g., sales performance, KPIs, and operational trends). 3. Advanced Analytics (Data Science Focus): Apply predictive modeling techniques to forecast future trends, sales, and inventory needs. Develop and deploy machine learning algorithms (e.g., regression models, classification, clustering, time-series forecasting) to identify patterns and optimize business operations. Utilize statistical techniques to uncover deep insights and correlations that drive strategic decisions. 4. Business Intelligence & Data Insights: Provide actionable business insights by combining descriptive analytics with advanced modeling techniques. Work closely with leadership to deliver data-driven recommendations that improve sales growth, operational efficiency, and customer insights. 5. Collaboration with Cross-Functional Teams: Collaborate with teams in marketing, finance, and operations to provide ad-hoc analysis and support data-driven initiatives. Work with IT and Data Engineering teams to enhance data infrastructure, ensuring easy access to clean, reliable, and high-quality data. 6. Continuous Improvement & Automation: Automate repetitive data extraction and reporting tasks to improve efficiency and reduce manual workload. Continuously refine and improve data models, ensuring they remain relevant as the business evolves. Required Skills & Qualifications: 1. Educational Background: Bachelor's degree in Data Science, Computer Science, Statistics, or a related field. Advanced certifications in Data Science or Machine Learning (e.g., Coursera, DataCamp, or similar) are a plus. 2. Experience: 3+ years of professional experience in data analysis or a similar role, with a solid foundation in business intelligence and reporting. Experience with predictive analytics, machine learning, and advanced statistical techniques in a business context. Proven track record in using data visualization tools (e.g., Power BI, Tableau, QlikView) to communicate insights to stakeholders. 3. Technical Skills: Programming Languages: Strong proficiency in Python or R (with experience in machine learning libraries like Scikit-learn, TensorFlow, etc.). Data Manipulation: Expertise in SQL, Excel, and data cleaning tools (e.g., pandas). Familiarity with big data technologies (e.g., Hadoop, Spark) is a plus. Experience with cloud platforms (e.g., AWS, GCP) and working with data pipelines. 4. Soft Skills: Strong problem-solving skills and the ability to work independently and with cross-functional teams. Communication skills to explain complex data insights in simple terms to non-technical stakeholders. Proactive, self-motivated, and adaptable with a keen eye for detail and accuracy.