Data Scientist

Role Overview:

Data Scientist

Key Responsibilities

• Algorithm Implementation: Implement advanced signal processing algorithms from research papers, including spatial filtering techniques, time-frequency analysis methods, and other cutting-edge approaches
• Data Preprocessing & Cleaning: Handle complex EEG/EMG datasets, perform signal filtering, artefact removal, and data preparation for analysis
• Model Development: Build and train machine learning and deep learning networks for neural signal classification
• Model Training & Optimization: Conduct hyperparameter tuning, cross-validation, and model optimization for various machine learning approaches
• Performance Evaluation: Implement metrics, validation frameworks and statistical analysis to evaluate model performance
• Documentation: Maintain comprehensive documentation of methods, results, and experimental procedures
• MLOps Implementation: Adopt best practices of MLOps for model training, evaluation and inference capabilities.
• Feature Engineering: Extract and analyze statistical features from EMG signals including spectral analysis, entropy measures, and channel dominance
  

Required Qualifications

• Experience: 2-3 years minimum in data science, machine learning, or signal processing roles
• Programming Skills: Proficiency in Python and optionally in MATLAB
• Deep Learning: Hands-on experience with TensorFlow, PyTorch, Keras frameworks or at least a strong experience with scikit-learn.
• Machine Learning: Strong foundation in supervised learning, particularly SVM and neural networks
• Time Series Analysis: Demonstrated experience working with time series data and signal processing techniques
• Implementation Skills: Excellent ability to translate research papers into working code implementations
  

Python

, MATLAB, TensorFlow, PyTorch, SVM ,CNN-LSTM

Preferred Qualifications

• Time series analysis experience in any domain (finance, IoT, sensor data is also acceptable)
• Strong mathematical background in signal processing or machine learning theory
• Experience scaling ML models with large datasets
• Knowledge of statistical feature extraction and spectral analysis
  

• Complete Ownership: Take full responsibility for your work, owning both successes and failures with accountability
• Collaborative Spirit: Genuine desire to help colleagues and contribute to the organization's success
• Data Enthusiasts: Someone who genuinely enjoys exploring data and finds satisfaction in uncovering insights
• Research Mindset: Curiosity to explore independent research directions and contribute to scientific knowledge
  

• Leadership Development: We mentor and encourage leadership qualities in every role we hire
• Industry Partnerships: Work with leading neuroscience labs and premier medical institutions to drive real-world impact
• Research Publications: Active involvement in publishing basic and applied neuroscience papers
• Conference Presentations: Opportunities to present research findings at scientific conferences
• Mentorship Opportunities: Future prospects to mentor interns and junior team members
• Independent Research: Encouraged and supported to pursue your own research interests You'll receive close mentorship on research methods while having the autonomy to implement and optimize solutions independently.