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.
About The Role
Autonomous Healthcare is looking for a skilled Machine Learning Engineer to join our data science team. This role is focused on diving deep into complex datasets to uncover hidden patterns and build predictive models related to pharmacy data. You will be a key player in developing and deploying solutions that directly impact our business, with a special emphasis on analyzing unlabeled data to detect critical anomalies. If you love solving challenging puzzles with data and seeing your models come to life in a production environment, we want to hear from you.
Key Responsibilities
- Design, develop, and train machine learning models to solve complex business problems.
- Perform in-depth data analysis and feature engineering on large, complex datasets, with a strong focus on unlabeled data to identify and investigate anomalies.
- Utilize Python and key libraries (such as Pandas, NumPy, and Scikit-learn) for data manipulation, analysis, and model building.
- Manage the end-to-end machine learning lifecycle, from data sourcing and model validation to deployment.
- Deploy and maintain scalable machine learning models in production on AWS (e.g., using SageMaker, Lambda, ECS/EKS).
- Collaborate with data engineers, software developers, and product managers to integrate ML models into our applications and systems.
- Monitor model performance, identify drift, and iterate on models to improve accuracy and efficiency.
Required Qualifications
- Proven professional experience as a Machine Learning Engineer or Data Scientist.
- Strong programming skills in Python and extensive experience with data science/analytics libraries, especially Pandas.
- Demonstrable experience in analyzing unlabeled data and building models for anomaly detection (e.g., using clustering, isolation forests, autoencoders, or other techniques).
- Practical familiarity with deploying machine learning models on AWS cloud infrastructure (e.g., AWS SageMaker, S3, Lambda).
- Solid understanding of core machine learning concepts, algorithms, and best practices.
- Excellent analytical and problem-solving skills.
Preferred Qualifications (A Plus)
- Familiarity with discrete event system simulation principles or tools.
- Experience with other MLOps tools and cloud services.
- A degree in Computer Science, Data Science, Statistics, or a related quantitative field.
