Operations Research; Machine Learning

About Autonomous Healthcare

At Autonomous Healthcare, we are at the forefront of medical innovation, developing the next generation of devices that will revolutionize patient care. Our mission is to commercialize breakthrough medical technologies by leveraging cutting‑edge AI and autonomous systems. We believe that the best solutions are built together, and we are looking for a key member to join our collaborative R&D team.

We are seeking a quantitative expert with a deep background in both Operations Research (OR) and Machine Learning (ML) to solve complex systems‑level challenges in healthcare. In this role, you will be a hybrid modeler and analyst, tasked with not only predicting outcomes but also prescribing optimal actions. You will develop sophisticated models to understand, predict, and optimize clinical and operational processes—from building machine learning systems that detect prescription anomalies to creating simulations that test the impact of new health policies.

This is a unique opportunity to apply a powerful combination of predictive and prescriptive analytics to make a tangible impact on healthcare.

• Develop discrete event simulation models to represent complex systems like patient journeys, clinical pathways, or pharmacy workflows.
• Conduct “what‑if” analysis using these simulations to forecast the impact of strategic decisions (e.g., changes to medication formularies, new intervention programs).
• Apply mathematical optimization and statistical modeling techniques to improve resource allocation, streamline processes, and recommend data‑driven solutions.

• Design, train, and deploy machine learning models to identify anomalous and high‑risk activities within large‑scale medication data.
• Leverage both unsupervised (e.g., clustering, isolation forests) and supervised (e.g., classification) techniques to solve complex detection and prediction problems.
• Perform advanced feature engineering to transform raw healthcare data into powerful signals for your models.

• Conduct exploratory data analysis (EDA) to form hypotheses and uncover underlying patterns in data.
• Write advanced, efficient SQL queries to extract and manipulate data from our enterprise data warehouse.

• Master’s degree in a highly quantitative field such as Operations Research ,
  Industrial Engineering ,
  Systems Engineering ,
  Computer Science ,
  Statistics
  , or a related discipline.
• Strong theoretical and applied knowledge in Operations Research , specifically in discrete event simulation ,
  stochastic modeling , and/or mathematical optimization
  .
• Proven expertise in Machine Learning , with hands‑on experience building and deploying models for classification ,
  clustering , and anomaly detection using libraries like Scikit-learn
  .
• High proficiency in Python and its data science ecosystem, including Pandas ,
  Num Py
  , and data visualization tools.
• Expert-level ability to write and performance‑tune complex SQL queries.
• Extensive experience using Jupyter Notebooks for model development, simulation, and analysis.