Remote Director of AI

Our client is seeking a strategic and hands-on leader to oversee the development and deployment of advanced machine learning, predictive analytics, and decision intelligence solutions that directly influence customer engagement, care recommendations, operational efficiency, and business outcomes.

This team sits at the intersection of data science, analytics, product, and business strategy, transforming complex healthcare and operational data into actionable decisions. The models and systems developed by this group drive critical business functions, including customer targeting, engagement optimization, recommendation engines, outcome forecasting, and performance measurement.

The organization is experiencing significant growth, expanding into new markets, customers, and service lines. As a result, the technology, processes, and team supporting these initiatives must scale rapidly while continuing to deliver on near-term business priorities. This role requires a leader who can balance immediate execution with long-term platform development, effectively navigate changing priorities, and build high-performing teams in a fast-paced environment.

This is a player-coach leadership position. The ideal candidate is equally comfortable defining organizational strategy, mentoring technical talent, engaging executive stakeholders, and contributing directly to technical architecture and modeling decisions when needed.

Primary Responsibilities

Lead a team of outcome-driven data scientists and ML engineers, with direct accountability for delivery, technical quality, and growth.

-Drive cross-functional partnership with Medical Economics, Clinical Operations, Product, and the Data & Intelligence Foundation team.
-Own the interface between modeling work and the platform and infrastructure it runs on.
-Make build-versus-buy and architecture calls, set the technical bar, and stay hands-on enough to make modeling decisions yourself.
-Other duties as assigned

Skills

-Deep applied ML: supervised learning on tabular and structured data, gradient-boosted trees (XGBoost, LightGBM), feature engineering, calibration, and rigorous offline and online evaluation.
-Production ML engineering: model packaging, deployment, monitoring, drift detection, and retraining pipelines. You own model quality in production, not just in a notebook.
-Strong software engineering fundamentals: Python, SQL, version control, testing, and code review standards you can set and enforce.
-Modern data and ML platform fluency: Databricks, dbt, and AWS (S3, Postgres, DynamoDB). Comfortable making build-versus-buy and architecture calls.
-Experimentation and causal rigor: A/B testing, uplift modeling, and the judgment to distinguish correlation from decision-relevant signals.
-Sound judgment on where newer methods (LLMs, agents, feature augmentation from external signals) add measured lift versus where they add cost and risk.
-You lead with the recommendation and state risks plainly, escalate risk early, and decide fast. Communication is concise and structured.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

-Master’s or PhD in a quantitative field (computer science, statistics, machine learning, operations research, applied mathematics, economics, or a closely related discipline). This is a requirement for the role; a PhD with applied, production-oriented research is a strong plus.
-A demonstrable history of ML systems you shipped to production that moved a business or clinical metric, with the specifics of what you built, what changed, and how it was measured.
-Evidence of delivering against hard external deadlines and managing data-dependency risk without slipping quality.
-A record of building and growing high-performing technical teams, including hiring, leveling, and developing data scientists and ML engineers.
-Experience owning a model portfolio across its full lifecycle, retiring or refactoring models that no longer earn their place.
-Ability and willingness to travel up to 10% as needed for onsite meetings, team collaboration, and company events.

-Healthcare, payer, or value-based care experience, and familiarity with HIPAA-regulated data.
-Experience translating actuarial or medical-economics concepts into model features and targets.
-Published or peer-reviewed work in applied ML, forecasting, or causal inference.

Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.