Senior Applied Machine Learning Analyst

Key Skills

C++, Data Analysis, Data science techniques, Machine learning techniques, Python Programming, R Programming, SQL

Job Description

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience managing projects and defining project scope, goals, and deliverables.
• 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 with Machine Learning systems.
• 2 years of experience in data analysis, including SQL or Python etc.

Preferred qualifications:

• Master's degree or equivalent practical experience.
• 5 years of industry experience analyzing, extracting, and visualizing valuable insights from datasets.
• 5 years of experience with one or more of the following languages (e.g., SQL, R, Python, or C++).
• Experience conceptualizing, designing, and building machine learning solutions that were integrated into production systems.
• Ability to deliver investigative recommendations that led to organizational adoption and measurable business impact.
• Excellent communication skills.

About the job

At Google we work hard to earn our users trust every day. 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 analytics team that partners with product and engineering teams across Cloud to deliver machine learning and data science solutions to stop bad actors while also enabling a trusted experience for our customers. As part of the GCP Protection Analytics team, you will leverage your machine learning, investigative, and business problem-solving skills to create solutions that address key abuse and security risks while also help our customers to seamlessly grow their businesses on Google Cloud. You will be responsible for identifying new business problems we need to build data science solutions to solve, to model building, to forging partnerships with other Cloud teams to ensure we can deliver high impact, industry-leading solutions. In this role, you will have the chance to design, implement, and improve machine learning and investigative solutions in production. You will build partnerships with Cloud product and engineering teams and deliver solutions that have improved Cloud customer experience and reduce abuse.

The US base salary range for this full-time position is $147,000-$216,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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.
• Understand abuse, security, and customer experience business problems our team addresses, to lead the ideation, design, and development of ML and investigative solutions.
• Perform data analysis to drive decision-making for our team, (e.g., 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 needed to evaluate model changes and launches, including coming up with new metrics as needed, and use those metrics to drive decision making.
• Communicate with technical and non-technical audiences at various levels of seniority, including producing investigative write-ups, dashboards, and data visualizations to convey your findings and recommendations to our team and cross-functional stakeholders.