Data Scientist Lead - Vice President

Job Responsibilities

• Perform data exploration and analysis to assess distributions, data quality issues, leakage risks, missingness, bias, and anomalies, and define data readiness criteria.
• C Conduct applied research to evaluate modeling approaches (classical machine learning, deep learning, and generative AI where relevant), and document findings, trade-offs, and recommendations.
• Build baseline models and iteratively improve performance through feature engineering, error analysis, and interpretability techniques.
• Design and deploy generative AI applications, including fine-tuning, Retrieval-Augmented Generation systems, and agentic AI frameworks.
• Build and maintain automated machine learning workflows for training, evaluation, packaging, deployment, and monitoring with a focus on reliability and reproducibility.
• Apply infrastructure-as-code practices to provision and manage AWS resources for AI and machine learning workloads.
• Collaborate with engineers to define deployment and integration patterns (batch, real-time, event-driven) and testing strategies.
• Design and implement testing strategies (unit, component, integration, end-to-end, performance, and champion/challenger where appropriate).
• Mentor team members on coding practices, AI and machine learning best practices, and maintainable implementation patterns.
• C Contribute to design reviews, operational readiness reviews, and documentation to raise overall engineering quality.
• C Support delivery in regulated environments by participating in documentation, reviews, and audit readiness activities.

Required Qualifications, Capabilities, and Skills

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field with 7+ years of relevant experience.
• Hands-on experience with data exploration and data validation (leakage, bias, missingness, outliers, and data quality) using frameworks such as PySpark, pandas, or Dask.
• Proficiency in Python for data science and modeling with production-quality coding practices and comprehensive testing.
• Proficiency with machine learning frameworks such as PyTorch, TensorFlow, PyTorch Lightning, or scikit-learn.
• Proficiency with cloud-based development on AWS.
• Experience applying natural language processing and large language model techniques such as prompt engineering, embeddings, and retrieval patterns.
• Experience building APIs (for example, FastAPI).
• Experience packaging and deploying containerized machine learning services (Docker; Kubernetes, ECS, or EKS).
• Experience operating on AWS services such as S3, IAM, CloudWatch, ECS, and SageMaker and/or Bedrock.
• Exposure to infrastructure-as-code tooling such as Terraform.

Preferred Qualifications, Capabilities, and Skills

• Experience delivering AI and machine learning solutions in a highly regulated environment.
• AWS certification.
• Knowledge of large language model evaluation methods, including quality, safety, guardrails, and reliability testing approaches.
• Familiarity with model serving patterns and operating models in production (deployment, observability, and support).
• Working knowledge of distributed compute platforms such as EMR or Databricks using PySpark for large-scale processing.