Sr. Data Scientist - Predictive Analytics

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Data Scientist - Predictive Analytics

Biofourmis is looking for candidates with machine/deep learning background to solve data science problems in digital health monitoring and emerging digital therapeutics.

Responsibilities:

• Design and architect state-of-the-art signal/image processing algorithms to derive novel biometric features and clinical parameters
• Design and architect machine/deep learning algorithms for disease diagnostics, detection of abnormal patterns in physiology and prediction of impending clinical conditions/events
• Design and conduct exploratory and statistical analyses to understand the complex human systems and health data
• Create reference benchmarks and processes to select appropriate models, libraries, and tools, and evaluate performance of algorithms using appropriate statistical methods
• Develop, train, and optimize end-to-end predictive analytic solutions with best-in-class computational efficiency and clinical performance
• Iterate and improve the model performances using appropriate transfer learning techniques
• Prepare technical reports for the algorithm designs & analytic solutions, implementations, and verification & validation
• Contribute to reports and presentations for nontechnical audiences that explain solutions workings and performance
• Research and keep up to date with solutions, datasets, and companies that are relevant to ongoing projects

• Follow the best practices of software development life cycle and produce high quality software systems

• Demonstrate and present to the cross functional teams and management
• Contribute to the company's intellectual property and publications

Qualification:

• PhD/MS in Data Science, Biomedical Engineering, Computer Science, Electrical Engineering or relevant discipline
• 1– 3 years of industrial/academia experience in developing predictive analytic algorithms for biosensor and or cloud deployment involving diverse data types – continuous/episodic medical/consumer device, electronic health records, images, and audio & video recordings
• Solid background and practical in digital/image/statistical signal processing and machine/deep learning; modeling experience in CNN, anomaly detection, clustering and time series forecasting is a plus
• Solid programming skills in Python; ability to write clean, concise, efficient and testable codes that follow coding standards and best practices
• Proficient with programming in Python libraries including Scikit-learn, Tensorflow, Pytorch, Keras and Pandas
• Solid background in machine-learning model explainability with SHAP, Feature Importance and Information Gain
• Experience with agile project management, version control, and QMS tools
• Strong problem-solving and excellent communication skills
• Good track record with patents and or publications