Understand the Tech Stack: You should have a clear understanding of the tech stack mentioned by the team. Make sure you're comfortable using the tools in the Python data stack, especially numpy, pandas, scikit-learn, scipy, statsmodels. If you're not familiar with any of these, take some time to practice. SQL and Data Analysis: The team uses SQL for data analysis, so ensure your SQL skills are sharp. You should be able to write complex queries and understand how to analyze data using SQL. Spark: Familiarize yourself with both PySpark and Scala Spark for data transformations. AWS Services: Brush up on your knowledge and understanding of AWS services, especially AWS Sagemaker, OpenSearch, Athena, Step Functions, EMR, and S3. If possible, try to get hands-on experience with these services. Understand the Projects: The projects mentioned provide a good indication of what you will be working on. Do some research to understand the technologies and methodologies used in similar projects. Data Literacy and Analysis: You should be able to demonstrate your experience in analyzing and transforming data, identifying data quality issues, testing hypotheses, and building models. Python and SQL Best Practices: You'll want to display a high level of fluency in Python and SQL, conforming to best practices. This means writing clean, efficient, and well-documented code. Soft Skills: A focus on teamwork, problem-solving abilities, and willingness to learn are key soft skills required for this role. Be prepared to provide examples of how you've demonstrated these skills in the past. Communication Skills: You should be able to articulate your analytical results and modelling approaches clearly to a wide range of audiences. Practice explaining complex concepts in simple, understandable terms.