CVM Modeling Specialist Professional

Job Summary

• Build predictive and descriptive analytical solutions makes use of relevant and available Teradata based customer database, large analytical table and offer data (i.e. historical behavior, offer attributes) to identify the best customer and offer instance combinations in line with business objectives.
• Develop, and implement the statistical models to predict customer uptake likelihood and upside impact for different offers, using data mining algorithms; such as decision tree, self-learning, Semi-Supervised, and Rule Driven Models.
• Monitor the outcome of already developed automated models and maintain the model accuracy along the time (model fine tune or gross model revamp).
• Interact with DWH and IT stockholder to deploy the developed analytical models in the production environment.
• Explanation and documentation of analytical model’s techniques and results.
• Dig in and become an expert in our datasets, provide data insights, drive key improvement initiatives and work with other teams to integrate results into working products.
• Enrich the Large Analytical Table from detail tables that reside in the Customer Database
• Write clean, scalable and fast performing code according to guidelines and quality standards (solid principles, code readability, and pattern use) and review other developers’ code.
• Design and implement the customer response modeling to identify segment-specific actions and increase campaign take-up rates.
• Presentation of analytics outputs to internal customers in a way that clearly conveys complex information.
• Development of SQL to enhance existing stored procedures.
• Maintain already developed automated end-to-end flow.
• Simple and convincing communication, especially around the analytical concepts for campaigns optimization.

Skills

• Extensive experience and hands-on of Machine Learning, and Deep-Learning techniques including: Support vector machines, Bootstrap aggregating / bagging, Cluster analysis, Cascading classifiers, Decision trees, Time series analysis & time series forecasting, Boosting, Factor analysis, Structural equation modelling, Item response theory, Markov chains, Voronoi diagrams, Neural networks, Genetic algorithms, Data visualization, Bayesian modelling, Multivariate regression, Logistic regression, etc.
• Experience of data management, implementation of predictive analytics especially for campaigns optimization and contextual modelling.
• Ability to attach complex business questions with data and curiosity to dive deep, identifying the root cause and “so what” rather than just the trends.
• Thrive in an environment that is tasked with providing data-driven decision support and business intelligence that is timely, accurate and actionable.
• Effective prioritize projects, manage multiple competing priorities simultaneously and drive projects to completion under tight deadlines.
• Effectively communicate with both business and technical teams.
• Writing SQL / PL/SQL
• Hands on experience in SAS, R , Python
• Experience with Data Presentation/Visualization tools (Tableau, JasperSoft, QlikView, MicroStrategy, Business Objects).
• Understanding of Hadoop Ecosystem components, Hadoop MapReduce framework, streaming processing frameworks.
• Knowledge of Hadoop-based analytical tools (Mahout, Hive, Pig. RHadoop, MOA, Jabatus, Alpine, etc.)

Education

Quantitative discipline – Computer Science Engineering/ Applied Mathematics/ Statistics.