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)