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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)

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