Data Scientist / ML Engineer

Role Description

We are looking for a Data Scientist / ML Engineer with strong Signal Processing expertise to support early product consulting and later Phase 3 model training and optimization. Initially, the specialist will consult on signal processing approach, model architecture, and data preparation strategy. At Phase 3 of product development, the same specialist will train, tune, and deploy real-time machine learning models. The product focuses on low-frequency physiological signals such as ECG, brain waves, and other biosignals, requiring signal filtering, transformation, and feature engineering for real-time AI solutions.

Key Responsibilities

• Phase 1-2: Consulting & Architecture
  • Analyze available signal data and product requirements
  • Define signal processing and filtering approaches
  • Recommend model architecture and ML approach
  • Define feature extraction strategy
  • Advise on real-time processing pipeline
  • Provide technical guidance on model feasibility and performance
• Phase 3: Model Training & Implementation
  • Process low-frequency physiological signals (ECG, brain waves, biosignals)
  • Apply signal filtering and mathematical transformations
  • Build feature extraction pipelines
  • Train and tune machine learning models
  • Optimize models for real-time inference
  • Support integration into production environment
  • Improve model accuracy and performance

Qualifications

• 3+ years as Data Scientist / ML Engineer
• Strong experience with Signal Processing
• Experience working with ECG, EEG, brain waves, or similar signals
• Experience filtering low-frequency signals (non high-frequency focus)
• Hands-on experience with:
  • Signal filtering
  • Noise reduction
  • Signal transformation
  • Feature engineering from signals
• Experience building real-time machine learning solutions
• Python skills (NumPy, SciPy, Pandas, Scikit-learn)
• Experience training and tuning machine learning models

Nice to Have

• Experience with biomedical signals
• Experience with time-series modeling
• Experience with real-time inference pipelines
• Familiarity with deep learning frameworks (PyTorch, TensorFlow)
• Experience working with edge devices or streaming data

Technical Skills

• Signal Processing
• Time-Series Analysis
• Real-Time Machine Learning
• Python (NumPy, SciPy, Pandas)
• Model Training & Optimization
• Feature Engineering
• Data Filtering & Transformation
• Statistical Modeling

Engagement Model

• Phase 1–2: Consulting / Advisory
• Phase 3: Model Training & Implementation
• Mid-level hands-on specialist
• Real-time signal-based AI product