We are looking for a Data Scientist who can work on real-world manufacturing and business problems using machine learning, statistics, and data engineering principles.

The role is not limited to building models. You will work closely with product, engineering, and manufacturing domain teams to convert industrial data into actionable intelligence.

You will contribute to:

- Forecasting production and demand
- Predictive maintenance
- Anomaly detection
- Optimization and recommendation systems
- KPI intelligence and root-cause analysis
- AI-driven decision support systems

This is a hands-on role suited for someone who enjoys solving practical industrial problems rather than working only on academic datasets.

Key Responsibilities

Data Understanding & Preparation

- Analyze structured and semi-structured industrial datasets
- Clean, preprocess, and transform ERP, MES, IoT, and machine data
- Perform feature engineering for time-series and operational datasets
- Build reusable data pipelines for ML workflows

Machine Learning & Analytics

- Develop predictive and prescriptive ML models
- Work on:
- Time-series forecasting
- Classification and regression
- Anomaly detection
- Optimization models

- Evaluate model performance and improve accuracy continuously
- Build explainable AI outputs for business users

Business Problem Solving

- Translate manufacturing problems into analytical models
- Identify operational bottlenecks and performance drivers
- Work on OEE, downtime, scrap, throughput, energy, and supply chain analytics
- Support ROI-oriented decision intelligence

Collaboration

- Work with backend engineers, frontend teams, and product stakeholders
- Collaborate with manufacturing experts to validate model outputs




- Participate in architecture and solution discussions

Deployment & Monitoring

- Support deployment of ML models into production systems
- Monitor model drift and performance degradation
- Contribute to MLOps and model lifecycle management

Required Skills

- Strong knowledge of Python
- Good understanding of:
- Pandas
- NumPy
- Scikit-learn
- SQL

- Understanding of machine learning fundamentals
- Experience with time-series analysis
- Knowledge of statistics and probability
- Familiarity with REST APIs and data integration concepts

Cloud & Engineering Exposure

- Exposure to AWS or Azure
- Understanding of Docker and Git
- Basic understanding of data pipelines and microservices architecture

Preferred Skills

- Experience with:
- PyTorch or TensorFlow
- Prophet / XGBoost / LightGBM
- TimescaleDB / PostgreSQL
- Stream processing or IoT data

- Manufacturing or industrial analytics exposure is a strong advantage

What We Expect

- Strong problem-solving mindset
- Ability to work with ambiguous real-world data
- Curiosity to understand manufacturing operations
- Ability to communicate insights clearly
- Ownership mentality and startup adaptability

What You Will Work On

- Predicting machine failures before breakdowns
- Forecasting production bottlenecks
- Dynamic line-capacity optimization
- Supplier risk intelligence
- Financial and operational KPI forecasting
- AI-assisted root-cause analysis
- Decision recommendation engines

Why Join PlantNxt

- Build AI for real factories, not demo datasets
- Direct exposure to manufacturing leadership problems
- Opportunity to work across AI, cloud, product, and industrial systems
- High ownership and learning environment
- Chance to shape the core intelligence layer of the platform

📌 Data Scientist (Chennai)
🏢 Plantnxt Technologies
📍 Chennai