Tax Data Systems and Process Analyst

Minimum qualifications:

• Bachelor's degree in Finance or Tax with an emphasis in Data Science/Information Systems or equivalent practical experience.
• 4 years of experience working with data, including SQL, data warehouses and dashboard applications.
• Experience with programming languages: SQL, Python, and Java.

Preferred qualifications:

• 5 years of industry experience in a data scientist, machine learning engineer, or software engineering role with a focus on machine learning applications.
• Experience in design, implementation, and delivery of scalable build/test/release software development cycle.
• Experience with data processing and management, including Relational Database Management Systems (RDBMS) such as Postgres or MySQL.
• Experience with machine learning frameworks like TensorFlow and Scikit-Learn.
• Effective written and verbal communication skills to translate technical solutions and methodologies to senior leadership.

About the job:

At Google, data drives all our decision-making. As a Data Scientist specialized in machine learning engineering, you will work on solving technical challenges across multiple tax areas.

As the Tax Technology team grows, we are particularly excited about bringing on folks interested in advancing their data science & MLE skills and collaborating with others to accelerate the creation of end-to-end data solutions to enable our tax partners to make informed decisions, manage risks and opportunities.

Responsibilities:

• Work cross-functionally with data scientists, data engineers and program managers to understand, implement and deploy machine learning pipelines.
• Develop and implement automation solutions using scripting languages (e.g. Python) to streamline processes and enhance efficiency, involving extensive API integration and management.
• Lead the development and deployment of full-stack technology solutions, including web applications (e.g., Java backend, TypeScript frontend, gRPC) and internal APIs, ensuring seamless data flow and system interaction.
• Improve machine learning scalability, usability and performance.
• Explore the state-of-the-art technologies and apply them to deliver business benefits.