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Metro Bangalore role with broad data and ML requirements attracts moderate competition.
Requires deep data architecture and specialized ML tooling, moderately limiting cross-industry transferability.
Explicit 7–10 years plus extensive mandatory data, ML, and tooling requirements increase strictness.
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Design and architect data pipelines and frameworks to support advanced analytics, machine learning, and AI workloads.
Develop, implement, and deploy statistical, AI, and deep learning models including regression, classification, and forecasting techniques.
Lead integration of probabilistic graph models, AI-driven solutions, and ensure data quality using frameworks like Great Expectations and Evidently AI.
7 to 10 years of experience in data architecture, data science, advanced statistical modeling, and AI architecture.
Advanced proficiency in Python and PySpark, and hands-on experience with SAS and SPSS.
Strong knowledge of statistical analysis including hypothesis testing (T-Test, Z-Test) and regression techniques (linear and logistic).
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related field.
Experienced in architecting and deploying AI solutions using frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, KubeFlow, and BentoML.
Skilled in scalable AI model deployment and lifecycle management including model monitoring and drift detection.
Demonstrates technical leadership in adopting emerging technologies and best practices in data architecture and AI.