Senior Data Scientist is responsible for driving new and innovative approaches as to how the company understands, interacts, and services its customers and distribution through the optimization of analytics to drive business insights. The Senior Data Scientist is expected to provide enterprise-level consulting to MetLife business executives on the design, development and implementation of complex analytical models and insights, and simplifying for business implementation/integration.
Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
Identify, hypothesize, and implement the analytics business problems, secure proof of concepts and project delivery
Deliver analytical solutions for the business, creating compelling explanations and plans to drive adoption in the business
Selecting features, building and optimizing classifiers using machine learning techniques
Use statistical methods to conduct descriptive, predictive and prescriptive model building, and summarize/present findings in an impactful manner.
Machine learning using state-of-the-art methods
Coordinate with different functional teams to implement models and monitor outcomes.
Support business leader’s benefits realization of improved sales (new business ANP), value of new business (VNB), operational efficiency, Net promoter Score (NPS), Customer Centricity and other agreed measures within the Data Analytics program
Team player, able to support wider needs in visualization, data management and BI as the need arises
Be part of the global analytics community, presenting and sharing best practices to improve global and local capability
5+ years of experience in the field of advanced quantitative techniques while working for leading global academic institutes or corporate innovation research labs or analytics organizations of large corporate or in consulting companies in analytics roles
Proven track record of professional success in advanced analytics role.
Very good Knowledge of the most important classical machine learning models (Classification, Regression, Clustering, Time-Series Analysis, Dimensionality Reduction etc.)