Data Scientist Time Series Analysis and Predictive Maintenance

**Role: Data Scientist - Time Series Analysis & Predictive Maintenance**

**Location: Pune, India**

**Client: Comsense Technologies**

**Experience: 4-6 yrs**

**About the Role**:
We are seeking a highly skilled and results-driven Data Scientist to join our dynamic AI and analytics team.

The role requires advanced technical skills in Python, SQL, cloud deployment (Azure), and MLOps practices, along with expertise in AI/ML techniques for time-series data.

**Key Responsibilities**:

- Time Series Analysis: Develop and deploy models for time-series forecasting, anomaly detection, and predictive analytics for process parameters.
- Predictive Maintenance: Create AI models to predict equipment failures and optimize maintenance schedules using sensor and process data.
- Data Processing & Feature Engineering: Extract meaningful features from time-series data, including signal processing and pattern extraction techniques.
- Process Optimization: Analyze manufacturing processes to identify patterns, improve efficiency, and reduce downtime using predictive models.
- Model Development & Deployment: Build, train, and deploy machine learning models on cloud or edge environments for production use.
- IoT Data Integration: Ingest, process, and analyze large volumes of data from industrial IoT devices and sensors for real-time analytics.
- Model Monitoring & Maintenance: Implement monitoring solutions to track model performance, detect data drift, and ensure high prediction accuracy.
- Forecasting Solutions: Develop models for demand forecasting and resource optimization in manufacturing environments.
- MLOps Practices: Set up and manage CI/CD pipelines for seamless model deployment and maintenance.
- Cloud Solutions: Design and deploy scalable AI solutions using Azure services, including Azure Machine Learning and Data Factory.
- Domain Collaboration: Work closely with manufacturing domain experts to understand process parameters and optimize AI-driven insights.
- Visualization & Reporting: Present complex data analysis and forecasting results through dashboards and visual reports for stakeholders.

**Required Skills & Experience**:

- Expertise in time series analysis, forecasting models (ARIMA, LSTM, Prophet), and anomaly detection.
- Strong knowledge of predictive maintenance models and techniques such as RUL (Remaining Useful Life) prediction.
- Proficiency in Python for machine learning model development and SQL for data manipulation.
- Experience with cloud services, particularly Azure (Azure ML, App Services, Data Factory).
- Strong understanding of IoT data processing and industrial protocols (MQTT, OPC-UA).
- Experience in signal processing techniques for sensor data.
- Knowledge of manufacturing processes and equipment monitoring.
- Familiarity with predictive analytics in industrial settings.
- Proficiency in MLOps tools and CI/CD pipelines for AI models.
- Experience in containerization using Docker and orchestration with Kubernetes.
- Knowledge of data engineering concepts and feature store integration.
- Strong understanding of version control (Git) and deployment pipelines.
- Excellent problem-solving skills and ability to communicate technical solutions to non-technical stakeholders.

**Preferred Qualifications**:

- Hands-on experience with edge deployment technologies for AI models in manufacturing environments.
- Familiarity with real-time streaming analytics for IoT data processing.
- Exposure to AutoML tools for rapid prototyping and production.
- Knowledge of SCADA systems and integration with AI-driven analytics.
- Background in statistical process control (SPC) for quality monitoring.
- Experience in feature extraction for industrial sensors such as vibration, temperature, and pressure data.
- Strong research and prototyping background in advanced time-series models and AI techniques.