Director, Machine Learning (Remote)

Responsibilities

• Lead and inspire a team of machine learning scientists and engineers, fostering a culture of innovation, collaboration, and professional growth.
• Serve as a hands‑on, \"at the coal face\" leader, actively contributing to the building, validation, and deployment of cutting‑edge machine learning models and systems.
• Define and execute the machine learning strategy and roadmap, ensuring alignment with business objectives and driving the delivery of our product pipeline.
• Architect and oversee the development of learning systems that integrate uncertainty, time, data structure, and knowledge under real‑world constraints.
• Collaborate with cross‑functional teams, including technology partners and business units, to translate business needs into practical and innovative AI/ML solutions.
• Champion and apply best practices and industry standards in AI/ML development, including MLOps, to ensure solutions are robust, scalable, and maintainable.
• Test and ensure ML solutions yield high‑quality, high‑confidence results in accordance with Quest's quality standards and regulatory requirements.
• Act as a trusted technical advisor and subject matter expert on machine learning to business customers and senior leadership.
• Promote a culture of continuous learning and self‑improvement to stay at the forefront of a rapidly evolving field.

Qualifications

Required Work Experience

• 10+ years of hands‑on experience in applied data science, machine learning, and AI development.
• 5+ years of technical leadership experience, including experience building and leading high‑performing teams of ML scientists/engineers.
• Demonstrable experience leading complex machine learning projects from initial concept through to successful operational deployment.
• A track record of scientific output, such as publications in top‑tier conferences or journals, is required for candidates without a formal PhD.

Preferred Work Experience

• Experience working with large‑scale healthcare data sets (e.g., clinical lab data, EHR, claims, genetics, pathology data).
• Experience working in a commercial, product‑driven environment.
• Experience with agile development methodologies (Scrum, Kanban).

Knowledge

• Critical: Deep knowledge of healthcare data and familiarity with healthcare ontologies (e.g., LOINC, ICD, SNOMED, HPO).
• Expertise in designing and building learning systems, with experience in several of the following areas:
  • Representation learning, including foundation models based on transformers
  • Graph neural networks (GNNs)
  • State machines and recursive learning
  • Bayesian hierarchical models
  • Hybrid neural‑symbolic systems
  • Probabilistic graphical models
  • Time‑aware and continuous time models (e.g., Hawkes processes)
  • Retrieval‑Augmented Generation (RAG) and Knowledge Augmented techniques
  • Out‑of‑distribution/drift detection and continual learning
• Strong knowledge of MLOps principles and tools for model deployment, monitoring, and lifecycle management.
• Proficiency in modern data science programming languages and libraries (e.g., Python, PyTorch, TensorFlow).
• Knowledge of cloud environments (AWS, Azure, or GCP).

Skills

• Exceptional leadership and team‑building skills with the ability to mentor and develop talent.
• A fundamental learning/self‑improvement mindset and a passion for staying current with the latest advancements in machine learning.
• Excellent communication and interpersonal skills, with the ability to convey complex technical concepts to both technical and non‑technical audiences.
• Strategic thinking and business acumen.
• Strong problem‑solving skills and the ability to navigate ambiguity.

People Leader

• This role is responsible for building and leading a team of Machine Learning Scientists and Engineers. Will have direct supervisory responsibility for 2‑3 employees initially, with the potential for team growth.

Work Environment

• Remote
• Travel up to 25% required

Required Education

• Bachelor's in Machine Learning, Computer Science, Statistics, or a related quantitative field with significant hands‑on experience

Preferred Education

A PhD in Machine Learning, Computer Science, Statistics, or a related quantitative field with significant hands‑on experience is strongly preferred. Exceptional candidates who have skipped formal PhD programs in favor of advancing their own knowledge and experience will be considered, provided they have a strong record of publications and scientific output.

• AWS Certified Machine Learning – Specialty
• Google Professional Machine Learning Engineer

Quest Diagnostics honors our service members and encourages veterans to apply.

Equal Opportunity Employer: Race/Color/Sex/Gender Identity/Religion/National Origin/Disability/Vets or any other legally protected status.

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