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Remote, mid-level Senior Data Scientist role with broad ML/MLOps requirements increases applicant competition.
Core ML, MLOps, and AWS skills are widely transferable across industries.
Explicit 5–8+ years, production ML experience, AWS and MLOps requirements make shortlisting stringent.
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Lead and deliver end-to-end data science and ML workstreams in 45-day agile release cycles, ensuring measurable business impact.
Select and apply diverse modeling approaches (statistical, ML, optimization, simulation, GenAI) tailored to business context and data.
Develop, validate, and deploy AI/ML platform components on AWS, including MLOps practices and collaboration with cross-functional teams.
Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Engineering, or related quantitative field.
5–8+ years of experience delivering data science, ML, or analytics solutions in production environments.
Hands-on experience with AWS-based ML and data platforms (e.g., SageMaker, S3, Glue, Redshift, Lambda) and MLOps tooling.
Work Experience Required: 5–8+ years in relevant production data science/ML roles. Notice period: Not explicitly mentioned in the JD.
Experienced in multiple modeling paradigms, including statistical modeling, classical ML, optimization, simulation, and GenAI.
Comfortable communicating technical trade-offs (performance, explainability, cost) to technical and non-technical stakeholders within agile and versioned release environments.
Proficient in Python/R/SQL with strong application of synthetic data techniques, offline experimentation, and controlled validation frameworks.