Principal Data Scientist - Credit Risk

When you join Verizon

You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the V Team Life.

What you’ll be doing...

As a Principal Data Scientist, you’ll lead projects that develop and perform complex analyses using Big Data technologies. You’ll be constantly on the lookout for new modeling techniques, evolving technologies, and emerging industry trends so that we can stay ahead of the game. Your work will help us meet our customers’ needs and make it even easier for them to do business with us.

Responsibilities include:​

• Making recommendations based on data with excellent visual representations of stories based on data for making critical business decisions.

• Exploring research in the data science field and utilizing open source research results for the improvement of customer facing products.

• Collaborating with cross functional teams i.e. engineers, managers, stakeholders and identifying opportunities to improve our products and customer experience.

• Developing comprehensive understanding of data and complexity of multiple systems to develop a seamless data pipeline which can visualize data at scale with cent percent accuracy.

• Building Machine / Deep Learning models.

• Working with Teradata, Hive, Spark, Tableau, Postgres, other big data systems with Verizon grid computing infrastructure.

• Mentoring and developing the team.

What we’re looking for...

You have strong analytical skills, and are eager to work in a collaborative environment with global teams to drive ML applications in business problems, develop end to end analytical solutions and communicate insights and findings to leadership. You work independently and are always willing to learn new technologies. You thrive in a dynamic environment and are able to interact with various partners and cross functional teams to implement data science driven business solutions.

You'll need to have:

• Bachelor’s degree or four or more years of work experience.

• Six or more years of relevant work experience.

• Experience as a data scientist or statistical modeler.

• Experience in one or more languages like Java, R, MATLAB, Python - generators, iterators, comprehensions, Numpy, Pandas, Matplotlib, Sklearn, Keras, Tensorflow, Pytorch.

• Experience with Machine Learning, Statistics and Probability, NLP, Deep Learning especially experience in recommendation systems, conversational systems, information retrieval, computer vision, regression modeling.

• Experience with Visualization tools Matplotlib, Seaborn, Tableau, Grafana etc..

• Demonstrated experience writing queries for reporting, analysis and extraction of data from big data systems.

Even better if you have one or more of the following:

• Experience applying machine learning in a credit or banking use case.

• Extensive experience in Loan Loss Forecasting, including developing PD, LGD, risk rating, and cash flow models.

• Experience automating and productionalizing credit model processes and documentation using gitlab & Sphinx.

• Ability to engage with larger credit organizations, including presentations, instructional meetings, and code reviews.

• A Master's degree in a quantitative discipline such as Mathematics, Statistics, Financial Economics/Econometrics, Engineering, Computer Science, or Operations Research.

• Experience in visual science / dashboard design principles.

• Experience in applying statistical ideas and methods to data sets to answer business problems.

• Experience with programming languages, like Python, SQL, or R.

• Experience with Machine Learning algorithms and tools.

If Verizon and this role sound like a fit for you, we encourage you to apply even if you don’t meet every “even better” qualification listed above.

In this hybrid role, you'll have a defined work location that includes work from home and a minimum eight assigned office days per month that will be set by your manager.

Scheduled Weekly Hours

40

Equal Employment Opportunity 

We’re proud to be an equal opportunity employer - and celebrate our employees’ differences, including race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, and Veteran status. At Verizon, we know that diversity makes us stronger. We are committed to a collaborative, inclusive environment that encourages authenticity and fosters a sense of belonging. We strive for everyone to feel valued, connected, and empowered to reach their potential and contribute their best. Check out our diversity and inclusion page to learn more.

Our benefits are designed to help you move forward in your career, and in areas of your life outside of Verizon. From health and wellness benefits, short term incentives, 401(k) Savings Plan, stock incentive programs, paid time off, parental leave, adoption assistance and tuition assistance, plus other incentives, we’ve got you covered with our award-winning total rewards package. For part-timers, your coverage will vary as you may be eligible for some of these benefits depending on your individual circumstances.

If you are hired into a California, Colorado, Connecticut, Hawaii, Maryland, Nevada, New York, Rhode Island, Washington or Washington, D.C. work location, the compensation range for this position is between $137,000.00 and $255,000.00 annually based on a full-time schedule. The salary will vary depending on your location and confirmed job-related skills and experience. This is an incentive based position with the potential to earn more. For part time roles, your compensation will be adjusted to reflect your hours.