Develop and implement machine learning models using state-of-the-art techniques and tools, including GenAI, RAG, and SageMaker, to solve complex problems and drive actionable insights.
Conduct data preprocessing, feature engineering, and exploratory data analysis to extract meaningful patterns and insights from large and complex datasets.
Collaborate with cross-functional teams to understand business requirements, formulate data-driven solutions, and communicate findings effectively to stakeholders.
Design and execute experiments to evaluate model performance, interpret results, and iterate on model improvements.
Deploy and maintain machine learning models in production environments, ensuring scalability, reliability, and performance.
Requirements:
Master's/Ph.D. degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
3+ years of experience in data science, machine learning, and predictive modeling, with a proven track record of delivering impactful solutions to real-world problems.
Proficiency in Python programming language and its scientific libraries (e.g., NumPy, Pandas, scikit-learn) for data manipulation, analysis, and modeling.
Hands-on experience with machine learning frameworks and libraries, such as TensorFlow, PyTorch, and XGBoost.
Strong understanding of statistical modeling, machine learning algorithms, and their underlying principles.
Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and services for data storage, processing, and model deployment.
