Analyst, Applied Machine Learning - Google
Seattle, WA
About the Job
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Seattle, WA, USA; Kirkland, WA, USA.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
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, Python, and/or C++.
- 5 years of experience with machine learning systems.
- Excellent written and verbal communication skills.
- Excellent problem-solving and critical thinking skills with attention to detail.
About the job
Trust & Safety team members are tasked with identifying and taking on the biggest problems that challenge the safety and integrity of our products. They use technical know-how, excellent problem-solving skills, user insights, and proactive communication to protect users and our partners from abuse across Google products like Search, Maps, Gmail, and Google Ads. On this team, you're a big-picture thinker and strategic team-player with a passion for doing what’s right. You work globally and cross-functionally with Google engineers and product managers to identify and fight abuse and fraud cases at Google speed - with urgency. And you take pride in knowing that every day you are working hard to promote trust in Google and ensuring the highest levels of user safety. 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.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
- Analyze and solve problems using data and statistical methods.
- Identify and prevent fraud and abuse.
- Improve tools and automated systems through data analysis, technical expertise, and present to stakeholders.
- Perform data analysis to drive decision-making for our team, such as sizing business problems, building machine learning models, leading the integration of machine learning models with business processes, and investigating the root cause of metric spikes.
- Own and drive outcomes for cross-functional data science projects, including building stakeholder relationships, developing project roadmaps, and leading teammates.
Source : Google