Key Responsibilities:

• Strategic Leadership Translate ambiguous business problems into concrete data science roadmaps and measurable key performance indicators (KPIs).

• Partner with product and engineering executives to align data strategy with corporate goals.

• End-to-End ML Architecture Design, build, and optimize large-scale predictive models, computer vision systems, or natural language processing (NLP)/Generative AI applications natively on AWS.

• MLOps & Productionization Architect automated machine learning pipelines (CI/CD for ML) to streamline data ingestion, model training, validation, serverless deployment, and real-time monitoring.

• Data Strategy & Infrastructure Collaborate with data engineering teams to design high-throughput data lakes and feature stores that support repeatable model training and low-latency deployments.

• Mentorship & Governance Set the technical standard for coding, documentation, and model governance.

• Provide rigorous code reviews and mentor senior and mid-level data scientists.

Required Technical Skills:

• Core AWS Machine Learning Absolute mastery of AWS SageMaker (including SageMaker Pipelines, Feature Store, Clarify for bias detection, and Model Monitor).

• Compute & Orchestration Deep experience using AWS Glue, EMR (Spark/PySpark on AWS), and AWS Lambda for serverless, distributed data processing and model execution.

• Data Storage & Querying Advanced proficiency with Amazon S3 (Data Lakes), Amazon Redshift, and Athena.

• Programming & Frameworks Expert-level Python and SQL. Mastery of ML libraries like Scikit-Learn, Pandas, and deep learning frameworks (TensorFlow, PyTorch, or Hugging Face).

• Generative AI (Modern Stack) Familiarity with Amazon Bedrock or AWS Trainium/Inferentia for leveraging, fine-tuning, and deploying Large Language Models (LLMs) and Agentic AI workflow frameworks (e.g., LangChain, LangGraph) is highly preferred.

Qualifications & Experience:

• Experience 10+ years of professional experience in data science, advanced analytics, and machine learning, with at least 4+ years dedicated to building and scaling production models on AWS.

• Proven Track Record Demonstrated history of owning at least 3+ major machine learning models through their complete lifecycle in a high-scale production environment.

• Communication Exceptional ability to distill complex mathematical and technical concepts into clear, actionable business strategies for non-technical stakeholders.