Senior Data Scientist - Insurance

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Team Overview

The Data Scientist is responsible for supporting the development of analytic solutions through consulting engagements for TransUnion’s clients across a broad array of credit, fraud, insurance and marketing business applications. This position requires a general understanding of US financial services risk management practice. The role will report to the Director of Science and Analytics. This is a hybrid position and involves regular performance of job responsibilities virtually as well as in-person at an assigned TU office location for a minimum of two days a week.

Role Overview and Core Responsibilities

• You will partner with internal and external cross-functional teams to drive new business initiatives and deliver long term value-added solutions to various TransUnion customers. These tasks will include –among others- the development of predictive models and business intelligence solutions of moderate scope and complexity for customers within the various industries served by TransUnion.

• You will lead analytic client engagements involving descriptive, predictive, and prescriptive analysis for a variety of business use cases leveraging multiple analytic techniques. As a project leader, you will take ownership of all aspects of the project from design, to development, to peer review, to client presentation, to implementation and audit.

• You will identify strategies and opportunities for customers to test and adopt TransUnion’s analytic products and services and develop project proposals, sales presentations and promotional collateral to enable the adoption of integrated customer solutions supported by TransUnion, applying your practical analytic experience to find areas of new value of TransUnion customers. In the context of these opportunities, you will interact directly with TransUnion Sales and business unit partners and provide an analytic perspective and general support as needed.

• You will design, write, and test programs for data extraction, segmentation and statistical analysis on large population datasets using languages such as R, Python, SQL, Hive and Spark, formalizing some of these efforts into repeatable process improvements.

• You will deliver analytic insights and recommendations in succinct and compelling presentations for internal and external customers representing a wide range of backgrounds and levels.

Required Knowledge and Experiences

• Master’s or PhD degree in statistics, applied mathematics, financial mathematics, computer science, engineering, operations research, or other highly quantitative field; or a Bachelor’s degree in a quantitative field with at least4 years of relevant professional experience. In either case, the candidate will demonstrate a consistent track record of academic excellence.

• Experience and demonstrated success in client-facing roles over a period of at least 1 year and at least 1 year of professional experience performing analytic work, preferably in industries served by Transunion. These experiences may or may not overlap.

• Advanced programming skills and aptitude; proficiency with statistical programs such as R or Python; experience with other programming languages (SQL, C/C++, Java, Ab Initio) and platforms (Hive, Spark); high level of familiarity with Microsoft Office tools.

We’re also looking for the preferred skills below. Whether you are proficient or could use some brushing up, we’re happy to support your development in:

• Proven ability to operate effectively under modest supervision in a complex and dynamic, matrixed environment.

• Familiarity with credit bureau data and solutions.

• Work experience in industries served by TransUnion, such as financial services, insurance, fraud, and digital marketing.

Pay Scale Information :

The U.S. base salary range for this position is $88,750 – $141,250 annually. *The salary range for this position reflects a reasonable estimate of the range of compensation for this job. At TransUnion, actual compensation is based on careful consideration of additional factors such as (but not limited to) an individual’s education, training, work experience, job-related skill set, location, and industry knowledge