Job Title: Data Scientist

Skills: Python, Machine Learning, Manufacturing Domain

Experience - 2-3 Years

Location: Mumbai, India

Job Description:

About Quation

We specialize in delivering synergistic solutions that enable businesses to make informed decisions. With years of cumulative experience, our leaders strive to create customized solutions that empower Fortune companies to make better decisions. We deliver tangible and measurable benefits through advanced analytics solutions.
Quation is a forward-thinking organization that is committed to delivering high-quality services to its clients. We are known for our expertise in specialized fields viz. Technology, Supply Chain Analytics, Data Engineering, Data Warehousing, and Marketing Analytics.
Our goal is to help businesses achieve their objective by providing them with the tools, information, and resources that they need to succeed. Quation provides exceptional customer service and builds long-term relationships.
Data is an asset! When harnessed, it effectively enhances performance, fosters efficiency, and accelerates growth in real-time.
At Quation, we believe that the field of analytics is constantly evolving. To stay abreast with the latest trends and technologies, we make significant investments in learning and development.
With deep domain expertise and core technical knowledge, our products and services provide our customers with a vital competitive edge. We create a world where understanding the complex intricacies of businesses becomes easier.
Our USP is our ability to leverage artificial intelligence to help businesses solve complex problems and make data-driven decisions. We analyze vast amounts of data quickly and accurately, providing insights that would be difficult or impossible to obtain through traditional methods.
We help businesses automate processes, improve efficiency, reduce costs, and gain a competitive advantage in the marketplace.

Key Responsibilities:

We are looking for an experienced Data Scientist to join our team to drive predictive maintenance and smart manufacturing initiatives using Machine Learning, Python, and IoT technologies. The ideal candidate will work closely with engineering, operations, and IT teams to analyze sensor data, build predictive models, and implement scalable ML solutions for real-time equipment monitoring and fault prediction.
Key Responsibilities:
Required Skills:
Preferred Qualifications:

• Develop and deploy predictive maintenance models using machine learning and statistical methods.
  
  
• Work with sensor/IoT data (vibration, temperature, pressure, etc.) to detect anomalies and forecast equipment failures.
  
  
• Build data pipelines for real-time and batch processing using Python and other relevant tools.
  
  
• Collaborate with domain experts and stakeholders to define KPIs and validate model outcomes.
  
  
• Implement end-to-end ML solutions from data ingestion to model deployment.
  
  
• Use time-series analysis, feature engineering, and deep learning techniques where applicable.
  
  
• Translate business problems into data-driven solutions in a manufacturing/industrial environment.
  
  
• Proficient in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch).
  
  
• Solid understanding of machine learning algorithms (regression, classification, anomaly detection, clustering).
  
  
• Experience with IoT platforms, protocols (MQTT, OPC-UA), and working with time-series data.
  
  
• Strong grasp of predictive maintenance use cases in a manufacturing context.
  
  
• Hands-on experience with data visualization tools (Plotly, Matplotlib, Power BI, etc.).
  
  
• Familiarity with cloud platforms (AWS/GCP/Azure) and MLOps is a plus.
  
  
• Knowledge of databases (SQL/NoSQL), and big data frameworks (Spark, Kafka) is a bonus.
  
  
• Bachelor's/Master’s degree in Data Science, Computer Science, Mechanical/Electrical Engineering, or a related field.
  
  
• Experience working in manufacturing, automotive, or industrial automation domains.
  
  
• Certifications in data science or cloud platforms are a plus.