MetLife

About The Role

MetLife is looking for a Data Science & BI Manager to become part of the Direct Marketing Team and work with the existing Data Experts in EMEA, to support in the construction of an easily exploitable data environment, to perform portfolio data and statistical analysis and predictive modelling.

Requirements

• At least, 6 years of experience doing quantitative and qualitative analysis.
• Bachelor’s degree in mathematics, Statistics, Actuarial Science, Engineering, Finance, or related fields.
• Fluency in SQL, R, Python programming languages.
• Ability to initiate and drive projects to completion with minimal guidance.
• The ability to communicate the results of analysis in a clear and effective manner.
• Very good level in statistical analysis and in predictive models
• Experience with machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with BI tools such as PowerBI and QlikSense.
• Fluent English, oral and written.
• Experience with large data sets (Big Data) and Digital (Hive/Hadoop)
• Data visualization – the ability to visualize data in different formats.
• Modelling of data, specifically around segmentation, defining segments, data decision modeling and loyalty segments.
• Dashboarding or creating executive dashboard.
• Experience in mapping databases/sources
• Research experience around statistical methods – regression etc.
• Mining and analyzing data.
• Working cross functionally to ensure all parties involved.
• Proactive approach to look for new solution for data analysis.
• Ability to quickly adapt to new methods, work under tight deadlines.
• Analytical and problem-solving skills
• Ability to work well within a team environment.

Job Description

• Extract data from different data sources (including web), structured data (classic database: SQL, Oracle, Cloud ....) And unstructured (texts, voice, image ...)
• Data cleansing and understanding, check coherence of data availability, highlight potential issues with data format, or informational changes.
• Build, enhance, and keep the data environment up to date.
• Choose and apply the segmentation, prediction and scoring algorithms appropriate for each problem (reporting, customer profiling, churn score ...)
• Supporting development and enhancement of analytical programs or statistical models for the applications in insurance business for internal use.
• Identify and implement/test new models or solutions to tackle specific issues.
• Real time optimization of Machine Learning processes
• Prepare and update reports, present results, and exchange with international team.
• Collaborate with stakeholders to understand business requirements and translate them into analytical solutions.
• Collaborate and provide support to sales and retention teams for increase in sales, predictive modelling, and other ongoing projects.
• May be involved in ad-hoc development of reports or dashboards to support other functions or across countries, working with experts located elsewhere or other local offices.