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Tier-1 brand, mid-level generalist ML role, metro location, and broad skillset increase competition.
ML, MLOps, and cloud engineering skills are broadly transferable across industries.
Role mandates specific ML, MLOps, cloud, and production deployment skills, leading to strict technical filters.
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Deliver end-to-end data science projects including exploratory data analysis, feature engineering, advanced modeling (e.g., XGBoost, TensorFlow, PyTorch), and model deployment.
Develop reproducible, high-quality code with robust error handling, model validation, and maintain code standards using Git.
Design and deploy scalable ML models with CI/CD pipelines, cloud data pipelines, model monitoring, and operational troubleshooting.
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or related quantitative field.
Proficiency in Python/R, SQL, and experience with data science libraries such as Scikit-learn; exposure to deep learning frameworks like TensorFlow or PyTorch.
Hands-on experience with cloud data storage and compute platforms (e.g., Google Cloud Storage, Vertex AI), and ML deployment tools (e.g., MLflow, Kubeflow).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in integrating complex data science solutions within enterprise technology environments and partnering closely with business stakeholders to deliver measurable impact.
Technical expertise in both data science modeling and software engineering practices, including cloud operations and MLOps to ensure production-grade scalable solutions.
Proven ability to communicate complex technical results to varied audiences and to mentor junior team members effectively.