Associate Director, Analytics Automation & Reusability

This role is responsible for building the tools, frameworks, and accelerators that enable the organization to scale AI, ML, and data science capabilities efficiently through automation and reusability. By transforming manual, one-off analytics into automated, production-grade solutions that can be reused across multiple use cases, this leader dramatically increases the speed and consistency of analytics delivery while reducing technical debt and enabling citizen data scientists to leverage enterprise-grade.

This position can be based remotely anywhere in the U.S. (there may be some restrictions based on legal entity). Please note that this role would not provide relocation as a result. The expectation of working hours and travel (domestic and/or international) will be defined by the hiring manager.

This position will require 20% travel.

• Design and implement workflow automation solutions for AI/ML and analytics processes (Airflow, dbt, Azure Data Factory).
• Build CI/CD pipelines for data science code, enabling automated testing, deployment, and monitoring of ML models.
• Design MLOps infrastructure to streamline the model development, validation, and deployment lifecycle.
• Build and maintain a library of reusable AI/ML components (feature engineering modules, model templates, prediction pipelines, visualization templates).
• Create standardized notebooks, scripts, and dashboards for common data science use cases that can be easily adapted for new projects.
• Build tools and platforms that enable business users and citizen data scientists to access AI/ML capabilities without engineering support.
• Implement automated testing frameworks for data science code (unit tests, integration tests, model validation tests, regression tests).
• Work with Infrastructure team to optimize performance and resource utilization of automated AI/ML solutions.

• Advanced degree in Computer Science, Data Science, Statistics, or related field; or Bachelor's degree with 8+ years relevant experience.
• 8+ years of experience in analytics engineering, data engineering, or software engineering roles.
• 3+ years of experience leading technical projects or small teams.
• Experience with workflow orchestration tools (Airflow, Prefect, dbt) and CI/CD tools (GitHub Actions, Jenkins).
• Knowledge of MLOps principles and tools (MLflow, Kubeflow, SageMaker).
• Experience with testing frameworks and code quality tools.
• Excellent documentation and communication skills.

• Experience in pharmaceutical, healthcare, or regulated industries.
• Background in both analytics/data science and software engineering best practices.
• Creative problem-solver who can identify opportunities for automation and reusability.
• Strong systems thinking with ability to design scalable, maintainable solutions.
• Experience building internal tools and platforms used by diverse user groups including citizen data scientists.

The salary for this position is expected to range between $176,400 and $327,600 per year.

Skills Desired Artificial Intelligence (AI), Biostatistics, Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Nlp (Neuro-Linguistic Programming) And Genai (Inactive), Organization Awareness (Inactive), Pandas (Python) (Inactive), Python (Programming Language), R Programming, Stakeholder Engagement, Statistical Analysis, Structured Query Language (SQL), Time Series Analysis