Develop and implement advanced data science models, including clustering, segmentation, random forest, gradient boosting (e.g. , XGBoost), ensemble techniques, and constrained optimization methods.
Explore additional approaches such as neural networks, support vector machines, and Bayesian methods to address diverse business challenges.
Design and execute matched-pair analyses, predictive analytics, and other statistical approaches to address business challenges.
Collaborate with stakeholders to identify use cases for advanced modeling and provide data science-driven solutions.
Engineer features and preprocess data to optimize model performance and interpretability.
Work with large datasets from diverse sources, ensuring data quality and consistency.
Perform model evaluation, tuning, and validation using appropriate techniques and metrics.
Deploy machine learning models into production environments, ensuring scalability and reliability.
Mentor and collaborate with team members to share knowledge and best practices.
Stay current on advancements in data science, tools, and methodologies to bring innovation to the team.
Qualifications :
4-8 years of work experience in classification, regression, clustering, association, dimension reduction, natural language processing (NLP), experiments, and optimization.
Proficiency in Python or R, with strong experience in libraries such as scikit-learn, TensorFlow, PyTorch, or similar.
Advanced knowledge of machine learning algorithms, statistical modeling, and data preprocessing techniques.
Strong foundation in mathematics, statistics, and computer science.
Proficiency in SQL for data extraction and manipulation.
Demonstrated ability to handle complex datasets and derive actionable insights.
Excellent communication and collaboration skills to work with cross-functional teams.
Experience deploying models into production environments is highly desirable.