Data Collection: Gather, clean, and preprocess large datasets from various sources, ensuring data quality and integrity.
Data Analysis: Analyze data using statistical methods and machine learning algorithms to identify trends, patterns, and correlations that can inform business strategies.
Model Development: Develop predictive models and algorithms using programming languages such as Python or R, and apply machine learning techniques to solve complex business problems.
Data Visualization: Create visualizations and dashboards using tools such as Tableau, Power BI, or Matplotlib to communicate findings clearly and effectively to stakeholders.
Collaboration: Work closely with cross-functional teams, including marketing, finance, and product development, to understand their data needs and provide actionable insights.