AI​/ML Scientist​/Remote

Position: AI/ML Scientist (Remote)

MRIoA is looking for an experienced and pragmatic AI/ML Scientist to drive the design and productization of machine learning solutions tailored to healthcare utilization management. 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 production readiness, 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. Requirements:

Skills and Experience:

• 3+ years of experience in applied ML or data science, with at least 1–2 years focused on integrating ML into software products.
• Strong Python programming skills and experience with ML libraries (e.g., scikit-learn, Tensor Flow, PyTorch, Hugging Face, XGBoost).
• Proven experience working with real-world healthcare data (e.g., claims, EHR, clinical text) and understanding of common data challenges.
• Experience applying ML to structured and unstructured data, particularly in classification, NLP, or time series forecasting.
• Solid understanding of model evaluation, validation, and operational considerations (e.g., scalability, explainability, monitoring).
• Excellent communication and collaboration skills, with the ability to work effectively in agile product development teams.

Preferred Qualifications

• Experience with MLOps tools and practices (e.g., MLflow, Sage Maker, 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…