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Tier-1 brand, mid-level generalist ML role, metro location, and broad required skillset.
Specialized ML, GenAI, PyTorch and MLOps skills limit cross-industry transferability.
Explicit years plus mandatory ML stack, MLOps and AWS requirements enforce strict filters.
Lead end-to-end machine learning projects using PyTorch, AWS SageMaker, and related ML frameworks to deliver scalable AI solutions.
Design, implement, and optimize statistical models and deep learning solutions with a focus on MLOps pipelines, including model training, evaluation, deployment, and monitoring using tools like MLflow and Docker.
Partner with stakeholders to translate business problems into technical solutions, develop business intelligence applications, lead technical initiatives, and mentor junior data scientists.
Minimum 4 years of experience as a data scientist.
5+ years experience with data querying and scripting languages such as SQL and Python; experience with statistical software like R.
Proficient with AWS technology stack including AWS Redshift, S3, EC2, Glue and ML deployment tools (CodePipeline, Lambda, Step Functions).
Experience building end-to-end machine learning solutions that generate measurable business impact.
Experienced in managing complex ML lifecycle including MLOps, containerization (Docker), hyperparameter tuning, and model monitoring at scale.
Skilled in modern large language models (LLM) frameworks and generative AI applications, indicating ability to work on cutting-edge AI products.
Able to operate across cross-functional teams and communicate complex ML concepts to technical and non-technical stakeholders effectively.