Director of Data Science

About the Role

The owns the end to end lifecycle of AI and machine learning initiatives that redefine revenue cycle management for healthcare providers. This leader is responsible for establishing a clear, actionable AI strategy that aligns with the company mission of fixing the financial health of the healthcare system. They translate complex regulatory and business constraints into robust data solutions that drive measurable value for both internal teams and external customers. The role requires deep healthcare domain fluency combined with advanced technical expertise in machine learning and production systems. They must foster a culture of innovation, discipline, and trust across data science and engineering organizations. Ultimately, this position ensures that AI becomes a strategic, revenue enabling force rather than an experimental add on.

Key facts

What you'll do

Define and execute the enterprise wide AI and machine learning strategy for Adonis, aligning every initiative with overarching business objectives and healthcare industry requirements.

Balance long term strategic AI investments with rapid delivery of incremental value, ensuring quick wins that immediately improve revenue cycle management outcomes.

Lead, mentor, and grow a high performing Data Science and Machine Learning team, setting standards for innovation, quality, and collaboration.

Establish and enforce a comprehensive AI governance framework, ensuring all solutions comply with healthcare regulations such as HIPAA and FDA requirements.

Design scalable, production ready AI and ML architectures that integrate seamlessly into Adonis platforms and deliver value to healthcare customers.

Quantify, communicate, and demonstrate the tangible financial and operational benefits of Adonis AI solutions to customers, partners, and internal stakeholders.

Champion MLOps excellence by standardizing processes for deploying, monitoring, and maintaining AI models in secure, reliable production environments.

Partner closely with Product, Engineering, Sales, and Customer Success teams to identify opportunities, prioritize initiatives, and ensure AI projects maximize customer value.

Drive the discovery and implementation of advanced machine learning techniques tailored to clinical workflows and revenue cycle optimization.

Act as the primary technical spokesperson for AI capabilities, articulating vision, progress, and impact to both internal and external audiences.

Champion data quality, infrastructure reliability, and model performance to ensure robust, ethical, and explainable AI outcomes.

Promote a culture of experimentation, continuous learning, and rigorous evaluation across the data science organization.

Ensure that all AI and ML initiatives are delivered on schedule, within scope, and aligned with compliance standards and company policies.

Build strategic relationships with academic, industry, and regulatory partners to stay at the forefront of AI applications in healthcare.

Requirements

Bring eight plus years of professional experience in AI and machine learning, with deep knowledge of techniques applicable to complex, regulated environments.

Substantial prior experience in healthcare technology, clinical workflows, or related fields is essential to understanding customer needs and regulatory context.

Demonstrate a proven track record of taking AI models and products from concept to production in complex, enterprise grade environments.

Have experience leading data science and machine learning teams, including mentoring, performance development, and cross functional collaboration.

Possess strong business acumen, with the ability to translate technical capabilities into tangible business value and communicate impact to non technical stakeholders.

Work effectively in a collaborative environment, holding strong opinions while remaining open to input from diverse stakeholders across the organization.

Communicate complex AI concepts clearly and persuasively to technical and non technical audiences, including executives, clinicians, and partners.

Demonstrate familiarity with healthcare regulations affecting AI deployment, including HIPAA, FDA guidelines, and other relevant compliance frameworks.

Show a strong understanding of ethical AI principles, governance frameworks, and responsible AI practices to ensure fairness, transparency, and accountability.

Nice to have

Preferred candidates have deep expertise in specific machine learning techniques relevant to healthcare data and revenue cycle optimization.

Experience with MLOps platforms, model monitoring, and automated retraining pipelines in production settings is highly valued.

Background in working with clinical data sets, billing, coding workflows, and payer interactions is considered a significant advantage.

Familiarity with emerging AI regulations and standards specific to the healthcare sector is a welcomed asset.

Practical notes

This is a full time position based in New York City.

The base salary range specified applies to this role, and total compensation includes additional programs and equity as described.

No visa sponsorship details are provided in this source material.