Senior Data Scientist

Encardio Rite Group; India; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Encardio Rite Group is seeking a Data Scientist to analyze high-frequency time-series data from IoT devices such as accelerometers, strain gauges, and tilt meters. You will preprocess data, engineer features, develop ML models, and integrate them into real-time systems.

Collaborate with engineers and domain experts to translate physical behaviors into actionable insights and deploy models via Docker APIs. Strong statistics and time-series modeling experience are valued.

Qualifications

• Degree in data science, CS, EE or related field as stated.
• Experience with time-series data and ML/AI tools beneficial.

Responsibilities

• Clean, preprocess, and denoise high-frequency time-series data from IoT devices
• EDA to identify anomalies and patterns in multi-sensor data
• Feature extraction in time-domain and frequency-domain
• Develop ML models for anomaly detection and predictive maintenance
• Integrate models into real-time pipelines and edge/cloud envs
• Containerize models and deploy via APIs (Docker, FastAPI/Flask)
• Monitor performance and implement retraining strategies
• Document data processes, features, and models

Skills

Strong analytical skills

Problem solving

Communication

Self-driven

Team collaboration

Education

Bachelor’s or Master’s degree in Data Science or related field

Tools

Python

Docker

FastAPI/Flask

GitHub Actions

Jupyter

Job description

Job Description:

Position

Data Scientist

About The Role

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 will collaborate closely with engineers and domain experts to translate physical behaviors into actionable insights. This role is ideal for someone with strong statistical skills, experience in time-series modeling, and a keen interest in understanding the real-world impact of models in civil and industrial monitoring.

Key Responsibilities

• Clean, preprocess, and denoise high-frequency time-series data from IoT devices
• Perform exploratory data analysis (EDA) and identify anomalies and patterns in multi-sensor datasets
• Design and implement time-domain and frequency-domain feature extraction pipelines
• Build machine learning models for anomaly detection, event classification, and predictive maintenance
• Collaborate with data engineers to integrate models into real-time pipelines and edge/cloud environments
• Containerize ML models (Docker) and deploy via APIs (FastAPI/Flask)
• Monitor model performance post-deployment and implement feedback loops and retraining strategies
• Document data processes, features, and models for reproducibility and knowledge sharing

Key Deliverables

• Preprocessing and feature extraction modules for sensor data
• High-performance ML models for anomaly detection and event classification
• Dockerized deployment packages and scalable inference APIs
• Analytical notebooks and dashboards (Streamlit, Grafana)
• Model monitoring reports and retraining pipelines
• Comprehensive data dictionaries and technical documentation

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Electrical Engineering, or a related field

Technical Skills

• Programming: Python (NumPy, Pandas, SciPy, Scikit-learn, PyTorch/TensorFlow), Bash scripting
• Time-Series & Signal Processing: FFT, DWT, STFT, Wavelets (SciPy, tsfresh)
• ML/AI Tools: Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow
• Visualization & Analysis: Jupyter, Matplotlib, Seaborn, Plotly, Grafana
• Deployment: Docker, FastAPI/Flask, GitHub Actions, ONNX/TorchScript
• Data Engineering: Kafka, S3, Athena/Trino, Airflow/Argo Workflows
• Monitoring: Prometheus, Grafana

Soft Skills

• Strong analytical and problem-solving abilities
• Excellent communication and collaboration skills
• Ability to present complex technical concepts clearly
• Self-driven with a proactive approach to learning and improvement