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Tier-1 brand, popular Data Scientist title, mid-level role, and metro location amplify applicant competition.
Core ML and MLOps skills are transferable across industries, though business-domain context moderately matters.
Multiple mandatory ML, MLOps, cloud and software-engineering skills drive stringent technical screening.
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Own end-to-end delivery of data science projects including exploratory data analysis, feature engineering, model building, validation, and deployment.
Design scalable MLOps pipelines with CI/CD, implement monitoring, alerting, and troubleshoot prediction service issues.
Collaborate with business stakeholders to translate needs into technical solutions and mentor junior data scientists through code and methodology reviews.
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or related quantitative field.
Proficiency in Python/R and SQL; experience with Scikit-learn and exposure to TensorFlow or PyTorch.
Experience with cloud platforms and tools such as GCS, Vertex AI, MLflow, Kubeflow, and data processing pipelines (e.g., Spark, Dataflow).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in delivering reproducible, production-quality data science code with strong software engineering practices including Git workflows and unit testing.
Comfortable operating across data science, MLOps, and cloud infrastructure environments to optimize cost, security, and reliability.
Able to influence project technical direction and communicate complex results effectively to both technical and non-technical business leadership.