Data Architect - R01570398

Data Architect

Job requirements 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 workloads - Develop and implement statistical and AI models, including regression (linear and logistic), classification algorithms, forecasting techniques (ARIMA, exponential smoothing), and deep learning architectures - Lead the integration of probabilistic graph models, advanced statistical tests (hypothesis testing, T-Test, Z-Test), and AI-driven solutions into production systems - Collaborate with data scientists and engineering teams to optimize data workflows and AI model deployment using tools such as KubeFlow and BentoML - Ensure data quality and integrity by implementing validation frameworks like Great Expectations and Evidently AI - Manage and optimize large-scale data processing and AI environments using Python, PySpark, R, and SAS/SPSS - Evaluate and select appropriate machine learning and AI frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet) for scalable model deployment - Provide technical leadership in the adoption of emerging technologies and best practices in data architecture, advanced analytics, and AI solutions Required Skills: - Advanced proficiency in Python and PySpark - Expertise in statistical analysis and computing - Hands-on experience with SAS and SPSS - Strong knowledge of hypothesis testing, T-Test, and Z-Test - Experience with regression techniques (linear and logistic) - Proficiency in probabilistic graph models - Familiarity with Great Expectations and Evidently AI for data validation - Forecasting expertise using ARIMA, ARIMAX, and exponential smoothing - Working knowledge of KubeFlow and BentoML - Experience with classification algorithms (Decision Trees, SVM) - Experience architecting AI solutions and deploying deep learning models Preferred Skills: - Advanced experience with ML and AI frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet - Expertise in distance metrics (Hamming, Euclidean, Manhattan) - Proficiency in R and R Studio - Experience with scalable AI model deployment in cloud environments - Knowledge of automated model monitoring, drift detection, and AI lifecycle management Desired Qualifications: - Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline - Certification 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)