Data Scientist(ML & Predictive Analytics)

Role Overview

We are looking for a

Data Scientist with expertise in classical machine learning and predictive analytics to build custom models for equipment manufacturing and industrial environments. You will analyze sensor, time-series, and operational data to solve critical challenges, including predictive maintenance, equipment failure prediction, quality forecasting, and process optimization.

Key Responsibilities

• Model Development:

Design, train, validate, and deploy end-to-end classical ML and time-series models using Python.

• Feature Engineering &
• Analytics:

Clean and transform structured, sensor, and time-series data from SCADA, PLC, MES, and ERP systems into actionable features.

• Problem Solving:

Evaluate and optimize algorithms based on business objectives, metrics, and interpretability requirements.

• Cross-Functional Collaboration:

Partner with manufacturing, process engineering, QA, and maintenance teams to translate operational bottlenecks into ML solutions.

• Deployment &
• Monitoring:

Deploy production-ready models into operational workflows and continuously monitor performance and reliability.

Technical Stack &

• Qualifications

Mandatory Technical Skills

• Classical ML:

Regression, Decision Trees, Random Forest, XGBoost, LightGBM, CatBoost, SVM, KNN, Clustering (K-Means, DBSCAN), Dimensionality Reduction (PCA), Anomaly Detection, and Ensemble Methods.

• Time Series Analysis:

ARIMA, SARIMA, Prophet, and time-series forecasting techniques.

• Programming &
• Libraries:

Python, SQL, pandas, NumPy, scikit-learn, SciPy, statsmodels, XGBoost, LightGBM, CatBoost, Matplotlib, Seaborn.

• Statistics &
• Data Prep:

Hypothesis testing, regression analysis, experimental design, feature engineering, and robust ETL/preprocessing pipelines.

Domain Experience &

• Qualifications
• Proven track record building and deploying ML models from scratch (

AutoML experience alone is not sufficient

).

• Hands-on experience handling structured industrial/sensor datasets.
• Strong communication and stakeholder management skills.

Preferred &

• Nice-to-Have
• Manufacturing Domain:

Exposure to Automotive, Heavy Engineering, Process Manufacturing, IIoT, OEE, Root Cause Analysis, or Downtime Reduction.

• Advanced Analytics:

Predictive Maintenance, Remaining Useful Life (RUL) modeling, Survival Analysis, or Digital Twins.

• Deployment &
• MLOps:

Docker, Cloud (AWS / Azure / GCP), Model Monitoring, Edge Analytics, or Streaming Data.