Data Scientist - R01572015

Job requirements

• Design and implement robust statistical models using advanced hypothesis testing, regression, and forecasting techniques to deliver actionable business insights
• Develop and optimize machine learning algorithms for classification, prediction, and probabilistic graph models utilizing Python, PySpark, and R
• Conduct comprehensive statistical analysis with SAS, SPSS, and R Studio to support data-driven decision-making
• Build, train, and deploy scalable models using ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
• Apply advanced time series forecasting methods, including exponential smoothing, ARIMA, and ARIMAX, to analyze trends and predict outcomes
• Streamline model deployment and lifecycle management in production environments using KubeFlow and BentoML
• Implement and validate data quality checks with Great Expectations and Evidently AI to ensure dataset integrity
• Present complex data findings to stakeholders, translating insights into actionable recommendations that drive business outcomes

• Advanced application of hypothesis testing methodologies, including T-Test and Z-Test
• Expert-level regression analysis (linear and logistic) for predictive modeling
• Proficient programming in Python and PySpark for data manipulation and model development
• Extensive experience with statistical analysis using SAS and SPSS
• Hands-on expertise in probabilistic graph models for complex data relationships
• Mastery of time series forecasting techniques (exponential smoothing, ARIMA, ARIMAX)
• Implementation of classification algorithms such as decision trees and support vector machines (SVM)
• Deep familiarity with ML frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet
• Calculation and application of distance metrics (Hamming, Euclidean, Manhattan)
• Skilled in R and R Studio for statistical analysis and visualization

• Practical experience with Great Expectations and Evidently AI for advanced data validation
• Proficiency in cloud-based model deployment tools such as KubeFlow and BentoML
• Background in large-scale data processing and distributed computing environments
• Expertise in feature engineering and model interpretability techniques
• Familiarity with cloud-based data science platforms such as AWS SageMaker, Azure ML, or Google Cloud AI Platform

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
• Certification in Data Science or Machine Learning from a recognized institution, such as Microsoft Certified: Azure Data Scientist Associate or TensorFlow Developer Certificate