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Tier-1 brand, common Data Scientist title, and mid-level experience create strong applicant competition.
Specialized ML/GenAI and MLOps skills are transferable across industries but require specific domain expertise.
Explicit 5+ years plus broad mandatory ML/GenAI, deployment, and cloud skills enforce strict filters.
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Design, develop, and deploy advanced machine learning, deep learning, and Generative AI models including LLM-based use cases.
Lead end-to-end analytical solutions from data extraction and feature engineering to model optimization, deployment, and lifecycle management.
Partner with stakeholders to ensure models deliver measurable business impact and support data-driven decision-making across the organization.
Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or related technical discipline.
Minimum 5+ years of hands-on experience in Data Science, Machine Learning, Artificial Intelligence, or related domains.
Strong programming skills in Python (including NumPy, Pandas, scikit-learn) and SQL, plus experience with deep learning frameworks like PyTorch or TensorFlow.
Experience deploying machine learning models in production environments using tools like MLflow, Docker, FastAPI, and familiarity with cloud ML platforms (Azure ML, AWS SageMaker, GCP Vertex AI).
Experienced professional capable of managing complex datasets and building scalable, reliable machine learning solutions in production with CI/CD and MLOps practices.
Proficient with cutting-edge AI technologies including Generative AI, Large Language Models, and familiar with tools like Hugging Face and OpenAI APIs.
Comfortable working collaboratively across business and technical teams to translate requirements into impactful AI-driven applications.