Experience Range: With at least 7 to 10 years of experience in data architecture, data science, advanced statistical modeling, and AI architecture Key Responsibilities:
Design and architect robust data pipelines and frameworks to support advanced analytics, machine learning, and AI workloadsDevelop and implement statistical and AI models, including regression (linear and logistic), classification algorithms, forecasting techniques (ARIMA, exponential smoothing), and deep learning architecturesLead the integration of probabilistic graph models, advanced statistical tests (hypothesis testing, T-Test, Z-Test), and AI-driven solutions into production systemsCollaborate with data scientists and engineering teams to optimize data workflows and AI model deployment using tools such as KubeFlow and BentoMLEnsure data quality and integrity by implementing validation frameworks like Great Expectations and Evidently AIManage and optimize large-scale data processing and AI environments using Python, PySpark, R, and SAS/SPSSEvaluate and select appropriate machine learning and AI frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet) for scalable model deploymentProvide technical leadership in the adoption of emerging technologies and best practices in data architecture, advanced analytics, and AI solutionsRequired Skills:
Advanced proficiency in Python and PySparkExpertise in statistical analysis and computingHands-on experience with SAS and SPSSStrong knowledge of hypothesis testing, T-Test, and Z-TestExperience with regression techniques (linear and logistic)Proficiency in probabilistic graph modelsFamiliarity with Great Expectations and Evidently AI for data validationForecasting expertise using ARIMA, ARIMAX, and exponential smoothingWorking knowledge of KubeFlow and BentoMLExperience with classification algorithms (Decision Trees, SVM)Experience architecting AI solutions and deploying deep learning modelsPreferred Skills:
Advanced experience with ML and AI frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNetExpertise in distance metrics (Hamming, Euclidean, Manhattan)Proficiency in R and R StudioExperience with scalable AI model deployment in cloud environmentsKnowledge of automated model monitoring, drift detection, and AI lifecycle managementDesired Qualifications:
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related disciplineCertification in Data Architecture, Data Science, or AI (e.g., Certified Data Professional, Microsoft Certified: Azure Data Scientist Associate, AI Architect certification)Certification in Machine Learning frameworks or platforms (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning – Specialty)
