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Data Scientist

2 weeks ago

Delhi, Delhi, India Encardio Rite Full time ₹ 5,00,000 - ₹ 8,00,000 per year
: 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)