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 insightsDevelop and optimize machine learning algorithms for classification, prediction, and probabilistic graph models utilizing Python, PySpark, and RConduct comprehensive statistical analysis with SAS, SPSS, and R Studio to support data-driven decision-makingBuild, train, and deploy scalable models using ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNetApply advanced time series forecasting methods, including exponential smoothing, ARIMA, and ARIMAX, to analyze trends and predict outcomesStreamline model deployment and lifecycle management in production environments using KubeFlow and BentoMLImplement and validate data quality checks with Great Expectations and Evidently AI to ensure dataset integrityPresent complex data findings to stakeholders, translating insights into actionable recommendations that drive business outcomesRequired Skills:
Advanced application of hypothesis testing methodologies, including T-Test and Z-TestExpert-level regression analysis (linear and logistic) for predictive modelingProficient programming in Python and PySpark for data manipulation and model developmentExtensive experience with statistical analysis using SAS and SPSSHands-on expertise in probabilistic graph models for complex data relationshipsMastery 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, MXNetCalculation and application of distance metrics (Hamming, Euclidean, Manhattan)Skilled in R and R Studio for statistical analysis and visualizationPreferred Skills:
Practical experience with Great Expectations and Evidently AI for advanced data validationProficiency in cloud-based model deployment tools such as KubeFlow and BentoMLBackground in large-scale data processing and distributed computing environmentsExpertise in feature engineering and model interpretability techniquesFamiliarity with cloud-based data science platforms such as AWS SageMaker, Azure ML, or Google Cloud AI PlatformDesired Qualifications:
Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related disciplineCertification in Data Science or Machine Learning from a recognized institution, such as Microsoft Certified: Azure Data Scientist Associate or TensorFlow Developer Certificate
