Experience Range: With at least 8 years of experience in data science, statistical modeling, and advanced analytics, including up to 12 years leading advanced data science initiatives Key Responsibilities:
Lead the design and implementation of advanced statistical models and machine learning algorithms to address complex business challenges and deliver actionable insightsDevelop, validate, and optimize predictive and forecasting models using techniques such as exponential smoothing, ARIMA, and ARIMAX to improve business forecasting accuracyConduct rigorous hypothesis testing, including T-Tests and Z-Tests, to inform experimental design and support data-driven decision makingCollaborate with cross-functional teams to define project requirements, ensure alignment with organizational objectives, and deliver impactful data science solutionsOversee data preprocessing, feature engineering, and data quality assessments utilizing tools such as Great Expectations and Evidently AIMentor and guide team members in the use of Python, PySpark, R, and machine learning frameworks including TensorFlow, PyTorch, and Sci-Kit LearnImplement and manage end-to-end data science workflows and model deployment pipelines using KubeFlow and BentoMLEvaluate and interpret model results, ensuring statistical rigor and effectively communicating findings to stakeholdersRequired Skills:
Python and PySpark for data analysis and model developmentStatistical analysis and computing using SAS or SPSSHypothesis testing methodologies including T-Test and Z-TestRegression techniques such as linear and logistic regressionDevelopment and deployment of machine learning models using TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNetProbabilistic graph models and classification algorithms including decision trees and SVMTime series forecasting methods including exponential smoothing, ARIMA, and ARIMAXDistance metrics such as Hamming, Euclidean, and Manhattan distancesR and R Studio for statistical computing and visualizationData validation and monitoring tools including Great Expectations and Evidently AIPreferred Skills:
Advanced model interpretability and explainability techniquesExperience with cloud-based data science platforms such as AWS SageMaker, Azure ML, or Google AI PlatformExpertise in MLOps best practices for scalable model deploymentDesign and implementation of deep learning architectures for structured and unstructured dataDesired 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 Certified Data Scientist or TensorFlow Developer Certificate)Relevant certification in statistical analysis tools or platforms (such as SAS Certified Advanced Analytics Professional or Microsoft Certified: Azure Data Scientist Associate)