Analyst within Credit Risk Management team identifying credit segmentation opportunities using statistical methods. Collaborating with teams to enhance credit decision process and policies.
About the role
- Leverage structured and unstructured data from various media and entertainment sources to prepare datasets for advanced analytics and modeling.
- Develop and deliver impactful analytical tools and solutions leveraging statistical modeling, machine learning, and data science to uncover business insights and support strategic decision-making.
- Design and apply advanced predictive and machine learning models; including clustering (K-means, hierarchical), classification (KNN, Naive Bayes, CART), time series forecasting, logistic regression, and econometric models to optimize pricing strategies, assess price elasticity, segment customers, and enhance revenue across channels.
- Leverage generative AI and large language models (LLMs) to develop and implement personalized content and messaging strategies across diverse media channels, enhancing audience engagement and campaign effectiveness.
- Assess and validate statistical models using appropriate performance metrics to ensure precision and accuracy such as accuracy, sensitivity, specificity, ROC, AUC.
- Analyze consumer behavior trends and shifts across various digital touchpoints; perform cross-channel attribution analysis to inform targeted retention strategies.
- Monitor and analyze key engagement metrics to assess the performance of subscriber onboarding programs and their impact on long-term retention.
- Interpret complex analytical insights and translate them into clear, actionable business strategies that improve business outcomes.
- Support scalable deployment of data products by following best practices in CI/CD processes and contribute to agile project management through tools like Jira for sprint planning, tracking, and team coordination.
- Collaborate cross-functionally with technical and non-technical stakeholders to gather requirements, define project scope, and lead data science initiatives, demonstrating strong communication, leadership, and team-building skills.
Requirements
- Master’s or PhD in data Science, statistics, computer science, or related quantitative field.
- 5+ years’ experience in data science roles with demonstrated impact on retention, engagement, or churn reduction.
- Advanced skills in Python/R, SQL, and experience with ML libraries (scikit-learn, XGBoost, TensorFlow/PyTorch).
- Strong background in building predictive churn models, CLV, causal inference and uplift modeling.
- Leverage cloud platforms (e.g., AWS, Azure) and big data tools (e.g., Spark, Hive, Databricks), staying current with evolving technologies and Databricks’ architecture for scalable data science workflows.
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