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Encardio Rite
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Data Scientist

Data Scientist

Encardio Rite

0 years

0 Lacs

delhi india

Posted:4 days ago| Platform:

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Skills Required

data analyze preprocessing engineering learning model development integration preprocess frequency iot sensors analysis extraction ml maintenance deployment docker monitoring design collaboration diagnostics visualization reports metrics documentation research python numpy scipy pytorch signal processing statsmodels tensorflow keras xgboost matplotlib plotly fastapi flask inference github onnx kafka querying airflow training

Work Mode

On-site

Job Type

Full Time

Job Description

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)

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Data Scientist