FeetMe is looking for an engineer with experience in machine learning and in the development of biomarkers and their validation to join our team! The data scientist will participate in the development of new digital biomarkers to highlight mobility disorders and disease progression related to multiple pathologies. Our connected and smart insoles estimate spatio-temporal gait parameters with pressure sensors and an inertial motion unit in real life and real time. The main scope is the analysis of clinical data to identify new patterns of gait disorders in daily life.
The position will require the candidate to use deep learning techniques, data mining and inferential statistics that apply to various applications where clinical training data may be limited. The participant will be involved in the validation of the biomarker. He/she will participate in the development of statistical validation plans in compliance with the requirements of the regulatory authorities.

He/she must have experience (7 years at least) using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models. Also, they must have experience in statistics including their application in clinical trials.

Responsibilities of this position include:
• Detection of walking patterns related to pathological symptoms from kinematic and pressure signals
• Develop data model and algorithms to characterize the longitudinal evolution of chronic diseases with daily life data
• Develop processes and tools to monitor and analyse model performance and data accuracy.
• Explore CNN / LSTM / RNN Neural Network applications for the development of novel clinical database modelling
• Participate in the validation of biomarkers in compliance with the regulatory authorities
• Participate in the validation plan of digital biomarkers by applying statistics (test – retest, intra rater, concurrent validity, MCID)
• Collaborate with software and computer groups to design and implement clinical data management, model training and inference flow
• Bring forward creative ideas, develop production code and provide support as needed.

We are seeking a candidate with the following skills:

• Master’s Level Degree and or Phd in applied mathematics
• Strong interest and rigor in R&D
• Prior experience with a few of the following models: Logistic Regression, Linear Regression, Support Vector Machines, Hidden Markov Models, Conditional Random Fields.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Experience in statistics in clinical trials and/or in the validation of biomarkers in compliance with the regulatory authorities
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
• Strong background in Machine Learning including the utilization of popular toolkits such as TensorFlow, Torch, etc.
• Strong analytical and quantitative problem-solving skills.
• Proficient in one or more programming languages such as MATLAB, Java, R, C++, or Python

• A drive to learn and master new technologies and techniques.
• Good oral and written communications skills to interact with other development and applications engineers daily