Role Overview

We are looking for a Senior Data Scientist with strong hands-on experience in Machine Learning, MLOps, Spark MLlib, and Predictive Analytics.

The ideal candidate should have experience developing, deploying, and maintaining scalable machine learning models, particularly customer analytics models such as Churn Prediction, Customer Lifetime Value (CLV), Propensity, and Affinity Modelling.

The candidate will be responsible for building end-to-end ML pipelines, working with large-scale customer datasets, and implementing production-ready machine learning solutions using Python, PySpark, Apache Spark, and cloud technologies.

Key Responsibilities

- Analyze large volumes of customer, transaction, web analytics, and third-party datasets.
- Perform data extraction, cleansing, transformation, feature engineering, and statistical analysis.
- Design and develop machine learning models using Python, PySpark, and Spark MLlib.
- Develop and implement customer analytics models, especially:
- Customer Churn Prediction
- Customer Lifetime Value (CLV)
- Propensity Modelling
- Customer Affinity Modelling

- Build scalable ML pipelines using Apache Spark and Databricks.
- Implement MLOps practices for model deployment, monitoring, versioning, retraining, and lifecycle management.
- Develop automated ML workflows and production-ready model pipelines.
- Build and manage data processing pipelines using Azure Data Factory.




- Deploy and maintain ML solutions on Azure or AWS cloud platforms.
- Apply classification, clustering, regression, and other predictive modelling techniques.
- Optimize ML models for performance, scalability, and accuracy.
- Collaborate with clients and cross-functional teams to understand business requirements and deliver actionable insights.
- Contribute to product development and continuous enhancement of customer analytics capabilities.

Mandatory Skills & Qualifications

- 5–9 years of experience in Data Science, Machine Learning, and Analytics.
- Strong hands-on experience in Python, SQL, and PySpark.
- Strong experience with Apache Spark and Spark MLlib.
- Hands-on experience in MLOps and production ML model deployment.
- Strong experience in Churn Prediction and Customer Analytics.
- Experience developing predictive models using classification, clustering, and regression.
- Valuable understanding of statistical modelling, data mining, and predictive analytics.
- Experience with TensorFlow, Keras, and Deep Learning.
- Experience working with Databricks and distributed computing environments.
- Experience with Azure cloud services, particularly Azure Data Factory; AWS exposure is also valuable.
- Familiarity with Git, Jenkins, CI/CD pipelines, and model versioning.

Skills: mlops,churn predicution,mllib

📌 Senior Data Scientist (ML / MLOps / Client Analytics) (Bengaluru)
🏢 Zorba AI
📍 Bengaluru