Zorba is the new age analytics provider that brings data driven decision to life for its clients. We believe in getting analytics executed on ground with the client team rather than just creating complex machine learning or predictive modelling algorithms. 

We offer services across the spectrum ranging from analytics training and setting up data centres to delivering analytics based strategic consulting solutions. One of the key value adds we do is with the speed at which we deliver solutions.

Headquartered in Mumbai, Zorba was conceived with an objective of creating value for businesses in near future rather than long term. We are experts in leveraging big data, building data driven solutions and operationalizing analytics at each stage of decision making process with following key offerings: 

•	Data Visualization and Dashboard design
•	Predictive Modelling Algorithms
•	Data Management and Transformation
•	Analytics based reporting solutions
•	Marketing and Business Process Optimization

Witnessing the stories of success and failure of numerous businesses at different stages, we came to the conclusion that every business can be successful if improvisation continues relentlessly. 

We want to make this process faster and smarter for our clients in their quest for growth by hand holding through data analytics and strategic leadership for effective decision making. 

You can follow us on :
https://www.quora.com/profile/Yash-Chaudhary-516
https://www.instagram.com/zorbaconsulting/?hl=en
https://twitter.com/ConsultingZorba
https://www.facebook.com/Zorbaconsulting/

Zorba Consulting India

Job Summary
We are seeking an experienced Senior Data Scientist – Client Analytics with strong expertise in Machine Learning, Deep Learning, Python, PySpark, SQL, and Azure Databricks.
The ideal candidate will have hands-on experience developing and deploying scalable machine learning models for customer and marketing analytics, including Customer Churn Prediction, Customer Lifetime Value (CLV), Propensity Modeling, and Affinity Modeling.
The candidate will work with the PeopleCloud Customer team, supporting a cloud-based Customer Data Platform (CDP) and Marketing Automated Operating System (MAOS). The role involves collaborating with global teams, working directly with clients, and delivering end-to-end data science solutions using advanced analytics and cloud technologies.
Key Responsibilities
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Analyze large volumes of customer, transaction, web analytics, and second- and third-party data to identify meaningful business insights.
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Perform data extraction, preprocessing, cleansing, transformation, statistical analysis, and feature engineering.
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Design, develop, and implement scalable Machine Learning and Deep Learning models using Python, PySpark, Spark MLlib, TensorFlow, and Keras.
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Build and optimize end-to-end machine learning pipelines using Azure Databricks and Azure Data Factory (ADF).
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Develop customer and marketing analytics models, including:
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Customer Churn Prediction
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Customer Lifetime Value (CLV)
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Propensity Modeling
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Customer Affinity Modeling
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Customer Segmentation
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Apply predictive analytics techniques such as classification, regression, clustering, and multivariate testing.
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Work with large-scale distributed databases and big data processing frameworks.
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Collaborate with business stakeholders, product teams, developers, QA, and delivery managers to understand business requirements.
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Own end-to-end implementation of data science solutions, from problem identification and data exploration to model development and delivery.
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Work directly with clients to translate business challenges into actionable analytical solutions.
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Support enhancements to the COSMOS Cognitive Marketing Intelligence Cloud Platform.
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Follow development best practices using Git, Jenkins, and CI/CD processes.
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Mentor team members and contribute to data science best practices.
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Required Technical Skills
Programming &amp; Data Processing
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Strong hands-on experience with Python, SQL, and PySpark.
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Experience working with large-scale datasets and distributed computing.
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Machine Learning &amp; Deep Learning
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Strong knowledge of machine learning algorithms and predictive modeling.
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Experience with Spark MLlib, TensorFlow, Keras, and Deep Neural Networks.
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Hands-on experience with classification, regression, clustering, statistical modeling, and data mining.
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Cloud &amp; Big Data
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Experience with Azure Databricks and Azure Data Factory.
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Understanding of Microsoft Azure cloud infrastructure.
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Familiarity with AWS is an advantage.
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Experience designing scalable ML pipelines using big data principles.
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Customer &amp; Marketing Analytics
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Experience developing Churn, CLV, Propensity, or Affinity models.
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Understanding of customer behavior, segmentation, and personalized marketing analytics.
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Development Tools
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Git and Jenkins.
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Understanding of continuous integration and version control practices.
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Qualifications
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5–9 years of overall experience in Data Science, Machine Learning, or Advanced Analytics.
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Strong practical experience developing and implementing ML models.
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Experience delivering end-to-end predictive analytics solutions.
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Strong analytical and problem-solving skills.
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Excellent communication, stakeholder management, and client-facing skills.
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Ability to work independently and collaborate with distributed onshore and offshore teams.
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Master's degree in Computer Science, Mathematics, Physics, or equivalent education/professional experience.
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Mandatory Skills
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Python
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SQL
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PySpark
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Machine Learning and Predictive Modeling
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Azure Databricks
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Azure Data Factory (ADF)
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Customer Analytics / Marketing Analytics
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Churn / CLV / Propensity / Affinity Modeling
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