Director Data Scientist Modeler AIG New York

Job Description: • 2-minute read •
Machine Learning / Predictive Modeling / Statistics AIG is seeking world-class dynamic Data Scientists to join our Science team that drives transformational change through evidence-based decision making at the company and in the industries that AIG operates. • Are you a curious high performer?

• Do you want to work on a team that values harmonious collaboration? • Is your desire to learn and reinvent? AIG Science is the hub for decision sciences at AIG. The Science team offers an excellent work environment that provides the opportunity to use cutting edge techniques, to engage worthy problems and to work with other world-class professionals. Position Summary Our ideal candidate should possess the following: • Very strong background in one or more computational areas (Computer Science, Statistics, Economics, Physics, Computational Linguistics) • Proven facility with multiple modeling techniques • Exceptional programming skills in one or more platforms • The drive to deliver on commitments and an openness to new ideas • Master Degree (Preferably a PhD) • 10+ years of relevant experience (will be flexed for exceptional candidates)


Position Requirements:
Responsibilities include but not limited to: • Understanding complex business challenges, designing scientific solutions, manipulating large data sets, using cutting edge machine learning or statistical modeling techniques and synthesizing insights • Building scalable solutions that create great business impact • Strong and effective communication (both written and verbal) with colleagues and business leaders • Continuously advancing your skills and those of others In addition, the ideal candidate needs to be familiar with the following techniques and tools, with an expert-level experience in some: • Expertise in one or more modeling/machine learning platforms as such as R, SAS, and Python • Experience with additional programming languages such as C++, Java, Matlab, Octave a plus • Classification methods (e.g., Neural Net, Logistic Regression, Decision Trees, KNN, SVM, Random Forest) • Regression methods (e.g., Linear, Nonlinear, Boosted Regression Trees ) • Clustering methods (e.g., K-means, Fuzzy C-means, Hierarchical Clustering, Mixture Modelling) • Time-series Modelling/Forecasting (e.g., AR, ARMA, GARCH, Exponential Smoothing) • Statistical Analysis (e.g., Hypothesis Testing, Experiment Design, Hierarchical Modeling, Bayesian Inference) • Familiarity with common computing environment (e.g. Linux, Shell Scripting) • Knowledge about Big Data related techniques (e.g., Map-Reduce, Hadoop, Hive, NoSQL) • Advanced skills in SQL

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