Data Scientist - Remote

We are looking for a Data Scientist to join our client's R&D team, a small group of scientists and engineers developing algorithms that help improve the health of millions around the globe. You will design, develop, and optimize algorithms on real-world multi-channel sensor data, working directly with the scientists who own the research. 

We are looking for a candidate with a strong theoretical foundation in the mathematics behind algorithm development, a solid background in mathematics, physics, electrical/biomedical engineering, or computer science, recent industry experience with sensor signals, and a passion for solving complex problems.

RESPONSIBILITIES 

• Design, implement, and optimize algorithms for data processing, analysis, and decision-making systems on multi-channel sensor data. 

• Process raw sensor signals: filtering, denoising, windowing, FFT/spectrogram analysis, and feature extraction from raw signals. 

• Combine multiple sensor channels recorded in the same time window into a single model. 

• Translate theoretical concepts into production-quality Python code. 

• Identify, assess, and account for confounding factors in data analysis and model validation. 

• Own the ML pipeline end to end, from raw data to validated model, including training on Google Cloud. 

• Analyze algorithm performance, complexity, and numerical stability. 

• Document algorithm design, assumptions, and limitations, and communicate results directly with the client's scientists. 

• Bachelor's degree (BSc) in Mathematics, Computer Science, Electrical/Biomedical Engineering, Physics, or a related field. 

• Strong mathematical background and the ability to independently take a problem from concept to implementation. 

• Solid programming skills in Python. 

• Recent hands-on experience with multi-channel time-series (e.g., 20+ sensor channels in the same time window) combined into a single model, with at least 2 projects in the last 24 months. 

• Practical signal processing on raw sensor data: filtering, denoising, windowing, FFT, spectrograms, feature extraction, and unsupervised feature selection. 

• Solid understanding of confounding factors and proven use in real projects, including the ability to identify, assess, and account for them in data analysis and ML models. 

• Experience with both CNN and non-CNN architectures, including LSTM, for sequential data. 

• End-to-end ownership of an ML pipeline, from raw data to validated model, including training in cloud environments (Google Cloud preferred). 

• Industry experience with large, noisy, real-world data (academic-only experience is not sufficient). 

• Language Proficiency: English - Spoken C1; Written B2+ 

• Nice to have:  

  + Experience working in a small R&D team (under ~15 people) or startup, collaborating directly with scientists. 

  + Master's degree (or currently pursuing) in a relevant field. 

  + Experience with biosignals or sensor-array data. 

  + Familiarity with algorithm optimization for performance and scalability. 

• Understanding of edge-device deployment (deployment itself is owned by MLOps). 

• Receive 100% salary from the onboarding date.

• Participate in company activities: Teambuilding, travel, vacation and other activities.

• Work with large and advanced systems, have the opportunity to develop comprehensive technology skills with complex problems, requiring high accuracy.