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 and Machine Learning Engineering (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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $116000 - $166000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
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 technologies and apply them to deliver business benefits.
Minimum qualifications:
- Bachelor's degree in Data Science, Computer Science, Information Systems, Finance, Tax, a related quantitative field, or equivalent practical experience.
- 4 years of experience working with data, including data science methodologies or machine learning applications.
- Experience developing or applying machine learning solutions (e.g., Generative AI, Natural Language Processing (NLP), or Large Language Models (LLMs))
- Experience coding in Python and SQL.
Preferred qualifications:
- Master's degree in Data Science, Computer Science, Information Systems, Statistics, or a related quantitative field.
- Experience building and deploying end-to-end machine learning models or predictive analytics in production (e.g., TensorFlow, Scikit-Learn).
- Experience in the design, implementation, and delivery of scalable build/test/release software development lifecycles (SDLC).
- Ability to work with cross-functional teams to drive the development and deployment of GenAI and LLM solutions.
- Effective written and verbal communication skills to translate technical solutions and methodologies to senior leadership.