Experience Range: With at least 4 years of experience in advanced data science, statistical analysis, and machine learning model development, including hands-on work with large datasets and production model deployment. Key Responsibilities:
Design, develop, and deploy advanced statistical and machine learning models using Python, R, and specialized frameworks to address complex business challengesConduct rigorous statistical analysis, including hypothesis testing, regression analysis, and probabilistic modeling, to extract actionable insights from large-scale dataImplement and validate data quality checks using tools such as Great Expectations and Evidently AI to ensure data and model integrityCollaborate with cross-functional teams to define data-driven strategies, translate business requirements into analytical solutions, and present findings to stakeholdersDevelop, optimize, and maintain forecasting models using techniques such as exponential smoothing, ARIMA, and ARIMAX to support business planningBuild, train, and evaluate classification and regression models using ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras, MXNet, CNTK)Deploy and monitor models in production environments using scalable cloud-native tools such as KubeFlow and BentoMLDocument methodologies and contribute to continuous improvement of analytics best practicesRequired Skills:
Advanced proficiency in Python and PySpark for data analysis and model developmentExpertise in statistical analysis and computing using SAS or SPSSHands-on experience with regression techniques including linear and logistic regressionStrong knowledge of hypothesis testing, including T-Test and Z-Test methodologiesProficient in building and interpreting probabilistic graphical modelsExperience with classification algorithms such as Decision Trees and Support Vector Machines (SVM)Skilled in forecasting techniques including exponential smoothing, ARIMA, and ARIMAXFamiliarity with distance metrics such as Hamming, Euclidean, and Manhattan DistanceWorking knowledge of R and R Studio for statistical modelingExperience with data validation and monitoring tools such as Great Expectations and Evidently AIPreferred Skills:
Experience deploying machine learning models using KubeFlow or BentoMLProficiency with deep learning frameworks such as TensorFlow, PyTorch, Keras, MXNet, or CNTKBackground in cloud-based analytics platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform)Exposure to automated machine learning (AutoML) workflowsDesired 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 provider (e.g., Microsoft Certified: Azure Data Scientist Associate, IBM Data Science Professional Certificate)
