Graduate Analyst - Data & Machine Learning Operations Graduate Analyst - Data & Machine Learning Operations Graduate Analyst - Data & Machine Learning Operations

Volkswagen AG

4d•Hybrid

About The Position

Volkswagen Financial Services, a wholly-owned subsidiary of Volkswagen Group, is the trusted key to mobility for its brand partners. We are committed to supporting the Audi, Ducati, and Volkswagen brands and their Dealers, specializing in providing accessible mobility solutions for its Customers. The company’s offerings include Retail Leasing, Retail Financing, Commercial Financing for new and used vehicles, and End-of-Term vehicle disposition. This position is Career Level 17P located in Reston, VA, with a Role Classification of Hybrid. Please note, we are not able to consider OPT/CPT candidates Launch Your Career with Our Graduate Program Are you ready to kick-start your career in a dynamic, fast-paced environment where learning and growth are at the forefront? Our Graduate Program is designed to equip ambitious, high-potential graduates with the skills, experience, and network needed to thrive in the industry. Over the course of 18 months, the graduate analyst will rotate through three business areas, gaining hands-on experience, working on impactful projects, and developing critical professional skills. With mentorship, structured training, and exposure to senior leaders, this program is the gateway to building a successful and fulfilling career. If you're a curious, motivated, and forward-thinking individual eager to make an impact, we invite you to be part of a program that invests in your growth and success.Role SummaryThe Data & Machine Learning Operations Graduate Analyst will participate in a structured rotational program across Machine Learning Operations (MLOps), Data Integration, and Business Intelligence (BI) & Data Modeling. This role is designed to provide a comprehensive understanding of how data and machine learning solutions are deployed, maintained, and optimized in a business environment. The analyst will collaborate closely with data scientists, engineers, and business users to ensure data systems and ML models are performing efficiently, securely, and deliver measurable business value.

Requirements

Computer Science, Data Analytics, Information Systems, Business Analytics, or related technical discipline.
Proficiency in Microsoft Office (Excel, PowerPoint, Word) for documentation and reporting.
Analytical problem-solving and critical thinking.
Ability to communicate technical concepts to non-technical stakeholders.
Strong attention to detail and commitment to data accuracy.
Collaboration and teamwork across technical and business teams.

Responsibilities

Support end-to-end deployment and maintenance of machine learning models.
Monitor and maintain model performance to ensure consistent output and reliability.
Maintain and optimize cloud-based data pipelines to support ML operations.
Collaborate with data scientists and engineers to enhance automation and model efficiency.
Implement best practices in MLOps, including version control, testing, and observability.
Document processes and create reports on ML performance metrics and insights
Design, develop, and deploy data integration solutions based on business requirements.
Troubleshoot and resolve production issues in a timely and efficient manner.
Implement and maintain data quality processes for warehouse systems.
Participate in code reviews and collaborate on data integration best practices.
Build prototypes and proof-of-concept solutions for new data initiatives.
Evaluate and refine leading-edge tools and technologies to enhance data integration performance.
Educate end users on metadata and data lineage to improve data transparency.
Utilize BI tools such as Business Objects, Tableau, and SAS to extract and manage data from enterprise systems.
Develop and maintain BI products, including dashboards, reports, and data universes.
Administer BI platforms and support software upgrades and performance tuning.
Apply advanced SQL skills and understand data warehouse modeling (fact/dimension structures).
Partner with business stakeholders to deliver data consultation and ad hoc reporting.
Provide training and documentation to enhance data literacy across teams.