Experience 4+ years Programming (Advanced): Python, SQL Machine Learning (Expert): Supervised and unsupervised learning, feature engineering, model development and evaluation Statistics (Advanced): Statistical analysis, hypothesis testing, experimentation, probability Data Analysis (Advanced): Exploratory data analysis, data preparation, visualization, and interpretation ML Frameworks (Proficient): Scikit-learn, Pandas, NumPy Deep Learning (Familiar): PyTorch, TensorFlow or similar frameworks AI/ML (Proficient): NLP, recommendation systems, classification, regression, clustering, or other applied ML use cases Cloud & MLOps (Familiar): AWS / GCP / Azure, model deployment, CI/CD, ML pipelines Tools: Git, Docker, Jupyter, Linux Translate business problems into data science and machine learning solutions. Explore, clean, transform, and analyze large and complex datasets. Develop, train, evaluate, and improve machine learning models. Perform feature engineering and identify meaningful patterns and insights from data. Design experiments and evaluate model performance using appropriate statistical and business metrics. Build proof-of-concepts and take successful models towards production. Collaborate with Data Engineers, AI Engineers, Software Engineers, and product teams to build end-to-end AI solutions. Work with structured and unstructured data across different business domains. Monitor model performance and identify opportunities for continuous improvement. Document methodologies, assumptions, experiments, and model outcomes clearly. Stay current with developments in machine learning, GenAI, and applied AI, and identify where they can create meaningful business value. Contribute to building reusable approaches, frameworks, and best practices for data science and AI projects.