Libraries/Tools: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib

Data Analysis & Visualization: Power BI, Tableau, Excel

Database: MySQL, PostgreSQL, MongoDB

Machine Learning / AI experience

Big Data Tools (optional): Hadoop, Spark

Languages: Python, Java, JavaScript, C++, C#, etc.

Frameworks: React, Angular, Node.js, Django, Flask, .NET (based on your stack)

Version Control: Git, GitHub

Databases: SQL & NoSQL

DevOps tools (optional): Docker, Kubernetes, Jenkins

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• A solid understanding of retail industry dynamics, including key performance indicators (KPIs) such as sales trends, customer segmentation, inventory turnover, and promotions.
• Strong ability to communicate complex data insights to non-technical stakeholders, including senior management, marketing, and operational teams.
• Meticulous in ensuring data quality, accuracy, and consistency when handling large, complex datasets.
• Gather and clean data from various retail sources, such as sales transactions, customer interactions, inventory management, website traffic, and marketing campaigns.
• Strong proficiency in Python for data manipulation, statistical analysis, and machine learning (libraries like Pandas, NumPy, Scikit-learn).
• Expertise in supervised and unsupervised learning algorithms
• Use advanced analytics to optimize pricing strategies based on market demand, competitor pricing, and customer price sensitivity.

Good to have skills:

• Familiarity with big data processing platforms like Apache Spark, Hadoop, or cloud-based platforms such as AWS or Google Cloud for large-scale data processing.
• Experience with ETL (Extract, Transform, Load) processes and tools like Apache Airflow to automate data workflows.
• Familiarity with designing scalable and efficient data pipelines and architecture.
• Experience with tools like Tableau, Power BI, Matplotlib, and Seaborn to create meaningful visualizations that present data insights clearly

Professional & Technical Skills:

• Strong analytical and statistical skills.
• Expertise in machine learning and AI.
• Experience with retail-specific datasets and KPIs.
• Proficiency in data visualization and reporting tools.
• Ability to work with large datasets and complex data structures.
• Strong communication skills to interact with both technical and non-technical stakeholders.
• A solid understanding of the retail business and consumer behavior.
• Programming Languages: Python, R, SQL, Scala
• Data Analysis Tools: Pandas, NumPy, Scikit-learn, TensorFlow, Keras
• Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn
• Big Data Technologies: Hadoop, Spark, AWS, Google Cloud
• Databases: SQL, NoSQL (MongoDB, Cassandra)

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