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Tier-1 brand, mid-level popular role, metro location, and broad ML requirements increase competition.
Core ML/AI skills are transferable across industries, but personalization and recommendation focus adds domain specificity.
Explicit 5+ years plus extensive mandatory ML, deployment, and framework requirements create strict screening.
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Design, develop, and deploy scalable machine learning models and data science solutions including recommendation systems, personalization, NLP, Generative AI, and Large Language Models.
Build and improve end-to-end ML workflows covering data preparation, feature engineering, model training, validation, deployment, monitoring, and optimization.
Collaborate with Data Engineers, ML Engineers, Product Managers, and business teams to deliver data-driven solutions and mentor junior team members.
Bachelor’s/Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field.
5+ years of experience in building data science models and machine learning solutions.
Proficiency in Python, R, Scala or similar; experience with ML libraries like TensorFlow, PyTorch, Scikit-learn, XGBoost, Keras, Spark ML.
Strong understanding of machine learning techniques across Regression, Classification, Time Series, Recommendation Systems, Deep Learning, NLP, LLMs, and MLOps concepts.
Experience in large-scale consumer platforms, especially in personalization, customer growth, advertising, or content recommendation.
Ability to work with large-scale datasets and distributed computing frameworks, and familiarity with production ML infrastructure and cloud-based ML platforms.
Capable of translating advanced research and AI/GenAI tools into practical business solutions and scalable ML architectures.