Analyst - Data Science

Data Analysis: Analyze large datasets using statistical methods and data visualization tools. Identify patterns, trends, and correlations to extract meaningful insights. Model Development: Build and implement predictive models using machine learning algorithms. Test and validate models to ensure accuracy and reliability. Data Preprocessing: Clean, preprocess, and transform raw data into a usable format for analysis. Handle missing data, outliers, and data quality issues. Feature Engineering: Identify and create relevant features for model training and analysis. Optimize feature selection to improve model performance. Programming and Tools: Utilize programming languages such as Python or R for data analysis and modeling. Work with data science tools and libraries (e.g., TensorFlow, Scikit-Learn). Collaboration: Collaborate with cross-functional teams, including business analysts, software developers, and domain experts. Communicate findings and insights effectively to both technical and non-technical stakeholders. Problem Solving: Define and frame business problems as data science challenges. Develop innovative approaches to solving complex analytical problems.