Principal Data Science Engineer

Key Skills

Analytical and Problem solving, AWS, Data Analysis, Data management, Database, Effective communication skills, Machine learning techniques, Mathematics, NumPy, Oracle, Pandas, PLSQL, Python Programming, R Programming, RDBMS, SAS, Scikit-learn, SQL, Statistics, Unix Operating system

Job Description

When you join Verizon

Verizon is one of the world’s leading providers of technology and communications services, transforming the way we connect across the globe. We’re a diverse network of people driven by our shared ambition to shape a better future. Here, we have the ability to learn and grow at the speed of technology, and the space to create within every role. Together, we are moving the world forward – and you can too. Dream it. Build it. Do it here.

What you’ll be doing...

In this role you’ll apply cutting-edge machine learning techniques and AI technology and will lead data science projects to reduce fraud loss and prevent future fraud loss across all Verizon wireless/wireline channels for both consumer and business line of businesses. You will work closely with a team of talented data scientists, Big Data engineers, and software developers and play a key role in developing and delivering the next generation AI/ML solutions to the fraud IT/business. 

You will:

• Create data models to predict customer behavior and risky events/transactions and use them to improve performance and predictability.
• Develop resolutions to complex problems, using your sharp judgment to develop methods, techniques, and evaluation criteria, enabling you to deliver solutions that make a big impact.
• Engage with key business stakeholders in discussions on business strategies and opportunities.
• Build strong working relationships and develop deep partnership with the other IT teams and business.
• Lead small-to-medium portfolios of data science projects and deliver innovative technical solutions to solve problems and improve business outcomes.
• Lead the design and development of machine learning/statistical models and ensure best performance.
• Work closely with engineers to deploy models in production both in real time and in batch process, and systematically track model performance.
• Be a subject matter expert on machine learning and predictive modeling and a mentor to junior data scientists.
• Drive technical innovation through active research and applications of new theories, techniques, and technologies.

What we’re looking for...

With an eye towards improving performance and predictability, you like the science of analytics. You’re able to communicate technical information to non-technical audiences, and you take pride in your ability to share your considerable knowledge with your peers.

What you'll need to have...

• Bachelor’s degree and 6 or more years of work experience.
• Hands on classification model development using Python or equivalent programming languages
• General SQL proficiency one or many (Postgres, Oracle, SQL Server, Teradata).
• Five or more years of experience in practicing machine learning and data science in business.
• Experience with Relational Databases.
• Experience in leading medium-scale data science projects and delivering end-to-end.
• Strong communication and interpersonal influencing skills.

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

• Master’s degree in a quantitative field or relevant field.
• Strong foundational quantitative knowledge and skills; extensive training in math, statistics, physical science, engineering, or other related fields.
• Development experience with PL/SQL and SQL*Plus.
• Experience with UNIX shell scripting using Python.
• Experience interacting with the AWS ecosystem
• Technical experience in machine learning and statistical modeling.
• Experience in data management and data analysis in relational database.
• Excellent problem solving and critical thinking capabilities.
• Strong experience in SQL and database management.
• Exposure to the scikit-learn, pandas and numpy ecosystems
• Experience with other data analysis software and scripting languages (e.g., R, SAS).
• Strong background in statistics.
• Fraud or risk management/modeling experience.
• Ability to bring in new technologies and streamline the existing and new applications.
• Ability to take immediate ownership of unanticipated fraud scenarios and quickly develop ad-hoc solutions when necessary.