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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