Engage on projects to guide data acquisition, storage, reporting, and analytics. Creation of data products for all consumers – business users, analysts, and modelers. Explore and understand data sets; visualize the data set; determine whether the data set has enough information to answer the question that the business is asking. Work with IT support to create ETL / ELT interfaces to the data lake and create and visualize the data and data products on the data lake; implement required data transformation in the data lake; configure required security and data masking to a data set; support testing of data acquisition, data set correlation, and / or model development. Investigate and resolve interface issues; work with IT to harden and productionize the model, model interfaces, and business procedures.
Master's Degree in Statistical Analytics, Data Science, or Actuarial Science. A Bachelor's Degree will be considered with at least three - five years of applicable work experience. Experience with coding tools such as R and Python. Exploratory data analysis, determination of correlation and predictive capabilties of the data, development of analytic models, and work with IT on developing interfaces to the data lake. Deep sense of curiosity, self-directedness and strong problem solving skills. Has strong verbal and written communication skills. Relevant technical skills include applied mathematics, statistics, calculus, quantitative or statistical methods or techniques, data mining, informatics, machine learning, data science, programming, computational algorithms, databases, artificial intelligence, natural language processing, bayesian inference, Markov logic, java, software engineering and/or systems design analysis. IT experience coding and testing programs and utilities to transport and transform data a plus. Experience in working with metadata management tools a plus. Relevant work experience in the following technical areas are required: Hadoop (Cloudera, Horton Works), relational databases and SQL, ETL development, spark, data validation and testing (Data Warehousing, ETL/ELT to the Data Lake, Using the Data Lake for data analysis (Hadoop tools – Hive, Impala, Pig, Sqoop, Hue, Kafka, etc., Open Source tools: Python, R, java). Knowledge of Cloud platform implementation (Azure or Amazon). Knowledge of data visualization tools is also a plus (AXA US uses Tableau on multiple platforms along with Python visualization in the Data Lake using Pandas and bokeh packages)