Autonomous Healthcare
Operations Research (Machine Learning)
Autonomous Healthcare, Santa Clara, California, us, 95053
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.
Job Summary 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.
Key Responsibilities 1. Modeling, Simulation & Optimization (Operations Research Focus)
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.
2. Predictive Modeling & Anomaly Detection (Machine Learning Focus)
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.
3. Foundational Analysis
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.
Required Skills & Qualifications
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
,
NumPy , 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.
#J-18808-Ljbffr
Job Summary 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.
Key Responsibilities 1. Modeling, Simulation & Optimization (Operations Research Focus)
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.
2. Predictive Modeling & Anomaly Detection (Machine Learning Focus)
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.
3. Foundational Analysis
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.
Required Skills & Qualifications
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
,
NumPy , 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.
#J-18808-Ljbffr