Lead Data Scientist

Lead Data Scientist page is loaded## Lead Data Scientist locations:
Chicago, Illinois time type:
Full time posted on:
Publicado hoyjob requisition :

** What We'll Bring:
** At Trans Union, we have a welcoming and energetic environment that encourages collaboration and innovation we’re – consistently exploring new technologies and tools to be agile. This environment gives our people the opportunity to hone current skills and build new capabilities, while discovering their genius.
The Financial Services Analytics team is an industry recognized, client-facing department that rewards an entrepreneurial spirit. We have deep technical expertise and an established reputation as an analytic solutions provider in the Financial Services industry. We have a wealth of data and industry experience within our large group of highly-trained analysts, statisticians, engineers, and economists. We also have a modern computing environment based on best-in-class “big data” technologies and the freedom to explore new data sources and statistical and machine learning methodologies.

All of these resources will enable you to help us deliver next-generation analytic solutions for our customers.
Come be a part of our team – you’ll work with great people, pioneering products and cutting-edge technology.
** What You'll Bring:
*** Master’s or PhD degree in statistics, applied mathematics, financial mathematics, engineering, operations research, or another highly quantitative field.  A track record of academic excellence
* At least four (4) years of professional experience performing analytic work in Financial Services or related industries
* Multiple examples of demonstrated success in client-facing roles over a period of at least three (3) years
* Advanced programming skills; mastery of a statistical language such as R or SAS; experience using other programming and data manipulation languages (SQL, Hive, Pig, Python, C/C++, Java); familiarity with relational, MPP, and/or Hadoop data management frameworks; proficiency with Microsoft Office tools
* Versatile interpersonal and communication style with the ability to effectively communicate at multiple levels within and outside the organization; ability to work in a collaborative, fast-paced environment
* Strong project management skills with the ability to manage multiple assignments effectively
** We'd Love to See:
*** An understanding of current industry challenges and trends at the level needed to proactively identify customers’ analytical needs and related business opportunities
** Impact You'll Make:
** You will apply your analytical skills to work on all aspects of the account lifecycle in the consumer credit domain on behalf of a diverse set of clients, ranging from marketing and propensity models for customer acquisition and retention, fraud detection solutions, credit risk models for acquisition and account management, cross-sell applications, portfolio models for regulatory applications, event-based trigger solutions, and strategy analyses of various kinds.

You will also develop complex analytic solutions directly with Trans Union customers, business partners and other departments.

This position is responsible for developing credit risk management and business intelligence analytic solutions through consulting engagements and research serving Trans Union’s clients. This position requires an understanding of US consumer lending and credit risk management practices.
* You will partner with internal and external cross-functional teams to drive new business initiatives and deliver long term value-added product propositions for B2B customers in the US financial services segment s includes but is not limited to the development of predictive risk management and business intelligence solutions for credit card issuers, auto & mortgage lenders, collections agencies and retail banks.
* You will lead analytic client engagements involving descriptive, predictive, and prescriptive analysis through the consumer lending portfolio lifecycle, leveraging a variety of techniques (e.g., segmentation, logistic regression, survival analysis, principal component analysis, Monte Carlo simulation, scenario…