Machine learning engineer Jobs in Liestal

Jobdescription

Are you a seasoned Data Scientist / Machine Learning

Engineer driven by complex data and a passion for patient health?

We are looking for you to take the lead in designing and validating

predictive algorithms using cutting-edge techniques and time series

data from medical devices, directly impacting diabetes

care!

General

Information : Start date :

1.2.26

latest Start Date : 1.4.26

Planned duration : 1.2.27

Extension (in case of limitation) :

possible

Workload : 100%

Home Office : mostly

onsite

Working hours : Standard

Tasks &

Responsibilities : Algorithm Design &

Prototyping : Design, develop, and validate predictive and

analytical algorithms for CGM data. Develop robust code using

advanced ML and statistical techniques to prove technical

feasibility.

Feasibility & Ideation :

Understand patient needs and creatively model potential algorithmic

approaches using real-world sensor data.

Data

Pipeline & Feature Engineering : Apply expertise in processing

and managing heterogeneous time series data originating from

medical devices. Execute rigorous data cleaning, imputation,

transformation, and sophisticated feature

engineering.

Technical Execution &

Modeling : Build and optimize machine learning models (e.g.,

XGBoost, Neural Networks, etc.). Write high-quality, efficient, and

reproducible Python code for data analysis, modeling, and

experimentation.

Collaboration : Provide

technical guidance within an Agile team framework to junior data

science colleagues. Work effectively within a multidisciplinary,

distributed team to translate project goals into actionable data

science tasks.

Communication & Reporting :

Synthesize complex technical results and present clear feasibility

findings to diverse stakeholders.

Must Haves :

Minimum of 5+ years of hands-on

experience as a Data Scientist or Machine Learning

Engineer.

Demonstrated experience or

robust academic background (Master or PhD is highly desirable) in

Data Science, Machine Learning, Statistics, or a related

quantitative field.

Strong Statistical

Foundation : Solid grasp of statistical principles, experimental

design, and model validation

techniques.

Advanced Python Proficiency : Strong

proficiency in Python and its core data science ecosystem : Pandas,

NumPy, Scikit-learn, TensorFlow / PyTorch, and

XGBoost / LightGBM.

Time Series Data :

Practical experience with the processing, analysis, and modeling of

time series data from physical sensors or monitoring

devices.

We thank you for your

application!