Senior Data Scientist-Client Analytics
Description
- Analyze large volumes of customer, transaction, web analytics, and second- and third-party data to identify meaningful business insights.
- Perform data extraction, preprocessing, cleansing, transformation, statistical analysis, and feature engineering.
- Design, develop, and implement scalable Machine Learning and Deep Learning models using Python, PySpark, Spark MLlib, TensorFlow, and Keras.
- Build and optimize end-to-end machine learning pipelines using Azure Databricks and Azure Data Factory (ADF).
- Develop customer and marketing analytics models, including:
- Customer Churn Prediction
- Customer Lifetime Value (CLV)
- Propensity Modeling
- Customer Affinity Modeling
- Customer Segmentation
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- Apply predictive analytics techniques such as classification, regression, clustering, and multivariate testing.
- Work with large-scale distributed databases and big data processing frameworks.
- Collaborate with business stakeholders, product teams, developers, QA, and delivery managers to understand business requirements.
- Own end-to-end implementation of data science solutions, from problem identification and data exploration to model development and delivery.
- Work directly with clients to translate business challenges into actionable analytical solutions.
- Support enhancements to the COSMOS Cognitive Marketing Intelligence Cloud Platform.
- Follow development best practices using Git, Jenkins, and CI/CD processes.
- Mentor team members and contribute to data science best practices.
- Strong hands-on experience with Python, SQL, and PySpark.
- Experience working with large-scale datasets and distributed computing.
- Strong knowledge of machine learning algorithms and predictive modeling.
- Experience with Spark MLlib, TensorFlow, Keras, and Deep Neural Networks.
- Hands-on experience with classification, regression, clustering, statistical modeling, and data mining.
- Experience with Azure Databricks and Azure Data Factory.
- Understanding of Microsoft Azure cloud infrastructure.
- Familiarity with AWS is an advantage.
- Experience designing scalable ML pipelines using big data principles.
- Experience developing Churn, CLV, Propensity, or Affinity models.
- Understanding of customer behavior, segmentation, and personalized marketing analytics.
- Git and Jenkins.
- Understanding of continuous integration and version control practices.
- 5-9 years of overall experience in Data Science, Machine Learning, or Advanced Analytics.
- Strong practical experience developing and implementing ML models.
- Experience delivering end-to-end predictive analytics solutions.
- Strong analytical and problem-solving skills.
- Excellent communication, stakeholder management, and client-facing skills.
- Ability to work independently and collaborate with distributed onshore and offshore teams.
- Masters degree in Computer Science, Mathematics, Physics, or equivalent education/professional experience.
- Python
- SQL
- PySpark
- Machine Learning and Predictive Modeling
- Azure Databricks
- Azure Data Factory (ADF)
- Customer Analytics / Marketing Analytics
- Churn / CLV / Propensity / Affinity Modeling
This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Skills
- Analytics
- AWS
- Azure
- Azure Data Factory
- Big Data
- CI/CD
- Cloud
- Data Mining
- Data Science
- Databricks
- Deep Learning
- Distributed Computing
- Feature Engineering
- Git
- Jenkins
- Keras
- Machine Learning
- Neural Networks
- Predictive Analytics
- Predictive Modeling
- PySpark
- Python
- Spark
- SQL
- Stakeholder Management
- TensorFlow
- Version Control