招聘
About the job
Google's leadership team hand-picks thorny business challenges, and members of Biz Ops work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.
In this role, your primary mission is to help our Global Business Strategy and Operations (GBS&O) partners make the best possible decisions using data. You will own the full data science life-cycle for ambiguous business problems from initial analysis to recommendation. You will drive value across the full data spectrum, from core BI, data pipelining, and dashboarding to advanced causal and correlational modeling. Your primary focus will be transforming our Policy, Risk, and Compliance strategy, moving beyond analysis to produce and present insights that directly influence business outcomes. You will need the agility to pivot and deliver solutions as high-stakes business priorities evolve.
The US base salary range for this full-time position is $163,000-$237,000 + 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
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Own the full data science life-cycle, applying your expertise in causal inference, predictive modeling, data infrastructure, and descriptive analytics to build frameworks that solve business problems.
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Lead analytics and strategy for the Policy, Risk, and Compliance partner team, overseeing the process from data pipeline and tool design to advanced statistical analysis and the generation of insights.
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Partner with stakeholders across Strat Ops and other Analytics teams to translate ambiguous, high-priority business problems into clear, data-driven strategies and decisions.
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Address ambiguous problems by diving into messy datasets, then use your expert toolkit (e.g., SQL, R, and Python) to find creative, scalable solutions and build automated data pipelines, BI infrastructure, and dashboards from the ground up.
Minimum qualifications
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Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
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4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
Preferred qualifications
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6 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
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Experience with ads policy, risk, or compliance.
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关于Google

Google specializes in internet-related services and products, including search, advertising, and software.
10,001+
员工数
Mountain View
总部位置
$1,700B
企业估值
评价
3.7
25条评价
工作生活平衡
3.8
薪酬
4.2
企业文化
3.4
职业发展
3.9
管理层
2.8
68%
推荐给朋友
优点
Excellent compensation and benefits
Smart and talented colleagues
Great perks and work flexibility
缺点
Management and leadership issues
Bureaucracy and slow processes
Constantly changing priorities and reorganizations
薪资范围
57,502个数据点
Junior/L3
L3
L4
L5
L6
L7
L8
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Junior/L3 · Data Scientist L3
0份报告
$176,704
年薪总额
基本工资
-
股票
-
奖金
-
$150,298
$203,110
面试经验
9次面试
难度
3.4
/ 5
时长
14-28周
录用率
44%
体验
正面 0%
中性 56%
负面 44%
面试流程
1
Application Review
2
Online Assessment/Technical Screen
3
Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Product Sense
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