About Arcanys

Arcanys launched in 2010 to help entrepreneurs and innovators from Australia, Europe and other parts of the globe accelerate their software development with dedicated remote engineers from the Philippines (and, more recently, Bulgaria).

Why work with us?

There’s a unique spirit to Arcanys—manifested in our amazing talents, passion for technology, and a solid focus on everyone’s career and personal development. As a company rooted in people, we make a point to offer exciting work opportunities and foster a culture that everyone can meaningfully contribute to.

Join us as a: Applied ML Scientist, Clinical Risk Modelling

As an Applied ML Scientist on our GLP-1 companion project, you develop statistical and machine learning models on de-identified electronic health record (EHR) data to predict GLP-1 treatment outcomes. You build, validate, and clinically interpret prognostic models inside our U.S. partner’s secure data environment, working with an obesity-medicine advisor.

In this role you’ll get to:

- Build retrospective patient cohorts from longitudinal EHR data.
- Engineer time-windowed features from medical records, structured to avoid temporal leakage.
- Develop and internally validate statistical and machine learning models for two outcomes: GLP-1 discontinuation and post-discontinuation weight regain.
- Apply clinical-prediction statistics: discrimination, calibration, and subgroup performance, reported to the standards clinicians expect.




- Translate model outputs into interpretable risk tiers with clinical advisors.
- Identify the most promising modelling approaches within a constrained research timeline, balancing scientific rigor with practical impact.
- Document datasets, modelling decisions, and validation results to ensure reproducibility and facilitate future production deployment.
- Collaborate closely with software engineers to ensure models can be integrated into production systems once validated.

What you’ll need to succeed:

- Background in statistics, biostatistics, epidemiology, health data science, machine learning, or a related quantitative discipline.
- Around 3-6+ years developing clinical or tabular predictive models, with strong applied statistics.
- Fluent in tabular ML (gradient boosting, regularized regression) and clinical-prediction methodology, especially calibration and validation.
- Experience engineering features from EHR or other longitudinal records, with a sharp eye for data leakage.
- Fluent in Python and the scientific Python ecosystem, including Pandas, NumPy, scikit-learn, and related libraries.
- Comfortable in restricted clinical data environments and with health-data privacy (HIPAA, GDPR).
- Able to balance scientific rigor with pragmatic decision-making in a fast-paced research setting.

It’s Great if you have:

- Survival or time-to-event modelling.
- Prior work with EHR or claims data (ICD, RxNorm), ideally in obesity or metabolic disease.
- Experience developing models intended for production deployment.

📌 Applied ML Scientist, Clinical Risk Modelling (New Delhi)
🏢 Arcanys
📍 New Delhi