AI/ML Scientist (Healthcare) - Remote

Who We Are - Motivated by Purpose. Powered by Clinical Expertise. Founded in 1983, we’re a clinically-driven, tech-enabled utilization management company offering expert clinical reviews, regulatory guidance, and actionable insights to healthcare organizations. Excellence starts with our people. Why Join MRIoA? We provide: Top-Tier Benefits & Support - We invest in you—through competitive compensation, comprehensive benefits, continued education, and flexible work options. A Tech‑Driven Culture with Heart - We are a clinician-guided, tech-driven utilization management company with a culture that is approachable, collaborative, and dedicated to excellence. Growth Opportunities - Whether you’re a care reviewer, clinician, or operations specialist, you’ll have access to meaningful development and mentorship. MRIoA’s health care benefits provide coverage when it’s needed — with a focus on programs that support and encourage healthy living and your overall well-being. Our benefits include:

• Fully remote
• Medical
• Dental
• Vision
• Paid Time Off (PTO)
• Paid Sick Leave (PSL)
• Paid Holidays
• Profit Sharing
• 401(k) Savings Plan
• Short-Term and Long-Term Disability
• Tuition and Scholarship Assistance
• Voluntary Life Insurance
• Pet Health Insurance

POSITION OVERVIEW This role is focused on translating proven ML research and existing algorithms into practical, scalable tools that improve operational efficiency, automate decisions, and enhance the internal and external customer experience. Working at the intersection of data science, healthcare operations, and product development, the ideal candidate excels at adapting state-of-the-art models to solve targeted, high-impact business problems. Major Responsibilities or Assigned Duties: Applied ML & Product Integration

• Translate business and clinical requirements into machine learning use cases focused on automation, decision support, and risk prediction in the utilization management domain.
• Adapt and optimize existing machine learning techniques—including classification, NLP, and time series modeling—to address specific operational workflows and data structures.
• Collaborate with developers to rapidly prototype and iterate on ML models with a focus on productionreadiness, scalability, and integration into customer-facing products.
• Contribute to the design of intelligent services (e.g., automated prior authorization, clinical rule learning, denial prediction) that directly impact product capabilities.

Data & Model Engineering

• Collaborate with data engineers to acquire, preprocess, and structure healthcare data from diverse sources (claims, EHR, clinical notes).
• Perform data wrangling and feature engineering to enable robust modeling pipelines.
• Evaluate and tune model performance using business-relevant metrics (e.g., precision, recall, F1, ROI), ensuring alignment with product goals and customer needs.

Cross-functional Product Development

• Partner closely with product managers, designers, and software engineers to embed ML capabilities into digital products and decision support tools.
• Develop documentation, model APIs, and integration specifications to support seamless model deployment in production systems.
• Provide insights and recommendations to support product roadmap decisions and feature prioritization.

Operationalization & Lifecycle Management

• Ensure ML solutions are reliable, maintainable, and explainable, supporting long-term operation in healthcare environments.
• Implement monitoring and retraining strategies to maintain performance and adapt to data drift.
• Align development with healthcare compliance requirements (HIPAA, HITRUST, SOC 2) and promote ethical use of AI.

Continuous Improvement & Innovation

• Stay up to date with emerging research in ML and health AI, identifying opportunities to apply new techniques pragmatically.
• Conduct competitive analysis of commercial and open-source AI/ML tools, identifying components to reuse or adapt.
• Contribute to internal knowledge sharing, helping build a culture of applied innovation and product-driven development.

Work Environment: Ability to sit at a desk, utilize a computer, telephone, and other basic office equipment is required. This role is designed to be a remote position (work-from-home). Diversity creates a healthier atmosphere: All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status, sexual orientation, gender identity or expression, marital status, genetic information, or any other characteristic protected by law. This company is a drug-free workplace. All candidates are required to pass a Background Screen before beginning employment. All newly hired employees will take a Drug Screen, as well as agreeing to all necessary Compliance Regulations on their first day of employment. Employees are required to adhere to all applicable HIPAA regulations and company policies and procedures regarding the confidentiality, privacy, and security of sensitive health information. California Consumer Privacy Act (CCPA) Information (California Residents Only):

• Sensitive Personal Info: MRIoA may collect sensitive personal info such as real name, nickname or alias, postal address, telephone number, email address, Social Security number, signature, online identifier, Internet Protocol address, driver’s license number, or state identification card number, and passport number.
• Data Access and Correction: Applicants can access their data and request corrections. For questions and/or requests to edit, delete, or correct data, please email the Medical Review Institute at [email protected].

Requirements: Skills and Experience:

• At least 3 years of experience in applied Machine Learning (ML) or data science.
• 1 year of experience integrating ML into software products.
• Experience working with real-world healthcare data, claims, Electronic Health Record (EHR), and clinical text.
• Experience applying ML to structured and unstructured data, particularly in classification, NLP, or time series forecasting.

Hard Skills

• Strong Python programming skills.
• Knowledge of ML libraries, scikit-learn, TensorFlow, PyTorch, Hugging Face, or XGBoost.
• Knowledge of model evaluation, validation, and operational considerations (e.g., scalability, explainability, monitoring).

Education

• Master’s degree in Computer Science, or a related field.
• PhD in Computer Science, or a related field.

Preferred Qualifications

• Experience with MLOps tools and practices (e.g., MLflow, SageMaker, Airflow, Docker).
• Familiarity with clinical coding systems (ICD, CPT, SNOMED) and interoperability standards (FHIR, HL7).
• Background in building AI features in healthcare SaaS or digital health products.
• Awareness of AI regulatory and ethical guidelines in healthcare (e.g., model interpretability requirements).

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