Google USA

  1. Data Collection and Cleaning: Collecting, cleaning, and preprocessing data from various sources to ensure accuracy and reliability for analysis.

  2. Exploratory Data Analysis (EDA): Performing EDA to understand data patterns, identify trends, and formulate hypotheses.

  3. Model Development: Developing predictive models using advanced statistical and machine learning techniques to solve complex business problems.

  4. Model Evaluation and Optimization: Evaluating model performance, fine-tuning parameters, and optimizing algorithms for better accuracy and efficiency.

  5. Insights Generation: Communicating insights and findings to stakeholders through reports, visualizations, and presentations.

  6. Collaboration: Collaborating with cross-functional teams including data engineers, software developers, and business analysts