Applied Machine Learning Analyst, Cloud GCP Protection Analytics

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
• 5 years of experience managing projects and defining project scope, goals, and deliverables.
• 2 years of experience with machine learning models, working with data, quality metrics, quality iterations

Preferred qualifications:

• Master's degree in a quantitative discipline.
• 5 years of experience with one or more of the following languages: SQL, R, or Python.
• 5 years of experience with machine learning systems.
• 5 years of industry experience analyzing, extracting, and visualizing valuable insights from large and diverse datasets.
• Experience conceptualizing, designing, and building high impact machine learning solutions that were integrated into production systems.
• Experience delivering investigative recommendations that were adopted by an organization and drove real-world business impact.

About the job

GCP Protection Analytics (GPA) is part of Google Cloud's organization of abuse and security experts working daily to make Cloud a safer place. We are a machine learning, data science, and investigative team that partners with Product and Engineering teams across Cloud to deliver machine learning and data science solutions to stop actors while also enabling a trusted experience for our customers.

As a member of the Google Cloud Platform (GCP) Protection Analytics team, you'll use your machine learning, analytical, and business problem-solving skills to create solutions that address key abuse and security risks while also empowering our customers to seamlessly grow their businesses on Google Cloud. You'll address everything from identifying new business problems we need to build data science solutions to solve, to model building, to forging strong partnerships with other Cloud teams to ensure we can deliver high impact, industry-leading solutions.

In this role, you will use machine learning, investigative, and business problem-solving skills to create solutions that address key abuse and security risks while also empowering customers to grow businesses on Google Cloud. You will address everything from identifying new business problems to build data science solutions to solve, to direct model building, to forging partnerships with other Cloud teams to ensure we can deliver high impact, industry-leading solutions.
The US base salary range for this full-time position is $139,000-$207,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

• Own and drive outcomes for cross-functional data science projects, including building stakeholder relationships, developing project roadmaps, and leading junior teammates.
• Develop an understanding of the abuse, security, and customer experience business problems, our team works on to lead the ideation, design, and development of machine learning and analytical solutions.
• Perform data analysis to drive decision-making for our team, such as sizing business problems, building ML models, leading the integration of ML models with business processes, and investigating the root cause of metric spikes.
• Identify the right metrics to evaluate model changes and launches, including coming up with new metrics, and use those metrics to drive decision making.
• Communicate with technical and non-technical audiences at various levels, including producing write-ups, dashboards, and data visualizations to convey findings and recommendations to our team and cross-functional stakeholders.