Data Scientist

Programming & Tools

• Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch)
• Working knowledge of statistical analysis and modeling

Machine Learning & AI

• Solid understanding of machine learning algorithms (supervised & unsupervised)
• Hands-on experience with: Regression (linear, logistic), Classification (decision trees, random forests, SVM), Clustering (K-means, hierarchical)

Predictive Modeling

• Proven experience in predictive modeling and forecasting
• Ability to build, validate, and deploy predictive models
• Strong understanding of:

Feature engineering

• Model evaluation techniques (ROC, precision/recall, cross-validation)
• Experience working with real-world datasets to derive actionable insights

Statistics & Data Analysis

• Strong foundation in statistics and probability
• Hypothesis testing, regression analysis, and statistical modeling
• Data cleaning, transformation, and exploratory data analysis (EDA)

• 
  Data & Deployment (Preferred
• )Experience with cloud platforms (AWS, Azure, or GCP
• )Familiarity with Docker/containers is a plu
• sExposure to MLOps practices (CI/CD for ML models