Data Scientist - R01572016

Data Scientist

Job requirements Experience Range: With at least 4 years of hands-on experience in advanced data science, including statistical analysis and machine learning, and up to 6 years in similar roles Key Responsibilities: 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 Required Skills: 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 Preferred Skills: 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 Desired Qualifications: 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