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Metro location and common Data Scientist title increase applicant density despite specialty requirements.
Specialized ML and MLOps skills transfer across industries but require specific tooling experience.
Multiple mandatory expert-level skills (Python packaging, PySpark, MLOps) make shortlisting highly selective.
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Develop and deploy machine learning models and predictive analytics to address business challenges.
Design, optimize algorithms for data processing, feature engineering, and pattern recognition; lead data exploration and visualization for actionable insights.
Own MLOps implementation focusing on system architecture, pipelines, monitoring, and deployment prioritization under time constraints.
Expert-level proficiency in Python including OOP, functional programming, decorators, packaging, and build tools like setup.py, pyproject.toml.
Medium to advanced expertise in PySpark with strong understanding of Spark internals and optimization.
Mandatory experience with MLOps including batch and real-time ML systems, deployment strategies, and monitoring key metrics.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building and packaging Python projects with dependency management and CI/CD pipelines, preferably in Azure or similar.
Strong understanding of MLOps architecture and deployment tradeoffs, able to manage pipelines and solve business challenges end-to-end.
Comfortable negotiating complexity of big data environments, especially using Spark and cloud tools like GCP Vertex AI and BigQuery (optional but preferred).