Position: Data engineer
Company: Intellias
Category: Data Science
Experience required: 3y
English level: upper
Published: 2021-09-01T00:00:00+03:00

**Project Overview:**
Our client is an American Fortune 500 company headquartered in Colorado. The company size is 10000+. It specializes in distribution and value-added services related to electronic components and computer products. Their team includes 19,600 employees worldwide across more than 300 locations.

Intellias is involved in its healthcare solutions, namely, the integration ones.

The client's advanced engineering group and wide-reaching network of technology partners are operating with a vast range of solutions required in the healthcare field. The client provides expert help at every stage of the product lifecycle: design engineering services, manufacturing services, logistics services as well as rapid deployment solutions.

**Below are just a few examples of their success in health care industry:**

- Medical imaging
- Sensor aggregation and integration
- Distance medical
- Patient check-in kiosk
- Public health surveillance
- Health-monitoring kiosks

**Responsibilities:**
- Use datasets in given format and explore transferability on MIMIC-IV public dataset.
- Compare with internal datasets statistics and judge algorithm performance.

**Requirements:**

- 5-7 years of experience, specifically with SQL databases
- Knowledge of R
- Experience in Python (scikit-learn package)
- Experience in handling and processing large multi-modal datasets (labs/vitals/reports/images)
- Experience in the management and handling of high frequently measured datasets (e.g. via indexing in SQL databases)
- Joining of multiple / diverse data frames
- Experience in creating informative descriptive statistics of high-dimensional datasets (medians, means, missingness etc.)
- Experience in analyzing longitudinal / time series data
- Experience in the development, validation and interpretation of machine learning models. E.g. Random Forest, Boosted Trees, Regularized Logistic - Regression. Cross-validation, Up- & Down sampling
- English - upper-intermediate