Data Scientist

Description: As a Data Scientist at Encardio, you will analyze complex time-series data from devices such as accelerometers, strain gauges, and tilt meters. Your responsibilities will span data preprocessing, feature engineering, machine learning model development, and integration with real-time systems. You'll collaborate closely with engineers and domain experts to translate physical behaviours into actionable insights. This role is ideal for someone with strong statistical skills, experience in time-series modeling, and a desire to understand the real-world impact of their models in civil and industrial monitoring.

• Responsibilities
• Sensor Data Understanding & Preprocessing
• Clean, denoise, and preprocess high-frequency time-series data from edge devices.
• Handle missing, corrupted, or delayed telemetry from IoT sources.
• Develop domain knowledge of physical sensors and their behaviour (e.g., vibration patterns, strain profiles).
• Exploratory & Statistical Analysis
• Perform statistical and exploratory data analysis (EDA) on structured/unstructured sensor data.
• Identify anomalies, patterns, and correlations in multi-sensor environments.
• Feature Engineering
• Generate meaningful time-domain and frequency-domain features (e.g., FFT, wavelets).
• Implement scalable feature extraction pipelines.
• Model Development
• Build and validate ML models for:
• Anomaly detection (e.g., vibration spikes)
• Event classification (e.g., tilt angle breaches)
• Predictive maintenance (e.g., time-to-failure)
• Leverage traditional ML and deep learning and LLMs
• Deployment & Integration
• Work with Data Engineers to integrate models into real-time data pipelines and edge/cloud platforms.
• Package and containerize models (e.g., with Docker) for scalable deployment.
• Monitoring & Feedback
• Track model performance post-deployment and retrain/update as needed.
• Design feedback loops using human-in-the-loop or rule-based corrections.
• Collaboration & Communication
• Collaborate with hardware, firmware, and data engineering teams.
• Translate physical phenomena into data problems and insights.
• Document approaches, models, and assumptions for reproducibility.
  
  

🎯 Key Deliverables

• Reusable preprocessing and feature extraction modules for sensor data.
• Accurate and explainable ML models for anomaly/event detection.
• Model deployment artifacts (Docker images, APIs) for cloud or edge execution.
• Jupyter notebooks and dashboards (streamlit) for diagnostics, visualization, and insight generation.
• Model monitoring reports and performance metrics with retraining pipelines.
• Domain-specific data dictionaries and technical knowledge bases.
• Contribution to internal documentation and research discussions.
• Build deep understanding and documentation of sensor behavior and characteristics.
  
  

🔧 Technologies

Languages & Libraries

• Python (NumPy, Pandas, SciPy, Scikit-learn, PyTorch/TensorFlow)
• Bash (for data ops & batch jobs)
  
  

Signal Processing & Feature Extraction

• FFT, DWT, STFT (via SciPy, Librosa, tsfresh)
• Time-series modeling (sktime, statsmodels, Prophet)
  
  

Machine Learning & Deep Learning

• Scikit-learn (traditional ML)
• PyTorch / TensorFlow / Keras (deep learning)
• XGBoost / LightGBM (tabular modeling)
  
  

Data Analysis & Visualization

• Jupyter, Matplotlib, Seaborn, Plotly, Grafana (for dashboards)
  
  

Model Deployment

• Docker (for containerizing ML models)
• FastAPI / Flask (for ML inference APIs)
• GitHub Actions (CI/CD for models)
• ONNX / TorchScript (for lightweight deployment)
  
  

Data Engineering Integration

• Kafka (real-time data ingestion)
• S3 (model/data storage)
• Trino / Athena (querying raw and processed data)
• Argo Workflows / Airflow (model training pipelines)
  
  

Monitoring & Observability

• Prometheus / Grafana (model & system monitoring)