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Data Scientist, Research, Responsible AI, Trust and Safety

Minimum qualifications:• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field or equivalent practical experience.• 5 years of experience in solving product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of experience with a PhD degree.Preferred qualifications:• Experience with machine learning (tools, prepare training sets, train classifiers), large language models, predictive modeling, causal inference, statistical data analysis or operations research with SQL and Python.• Experience using mathematical techniques or statistical tools to find answers and translate results into business recommendations.• Experience with fraud, security and threat analysis in context of Internet-related products/activities, especially with Generative AI.• Excellent written and verbal communication skills with ability to self-direct and collaborate with stakeholders.• Excellent teaching skills, with ability to learn new techniques across offices and time zones.• Excellent problem-solving and critical thinking skills with attention to detail.About The JobTrust and Safety is Google’s team of abuse fighting and user trust experts working to make the internet a safer place. A diverse team of Analysts, Policy Specialists, Technical Experts, and Program Managers, we work to reduce risk and fight abuse across all of Google’s products, protecting our users, advertisers, and publishers across the globe in over 40 languages. Within the Trust and Safety organization, Data Science is part of the Insights and UX teams that leverage the power of data and research to deliver insights to inform selection-making, motivate operational excellence and foster user trust in Google products.In this role, you will evaluate and improve Google's products for users through well-reasoned research and analysis. You will understand and quantify how far the source can be trusted, apply critical thinking to the data, and think through the impact on a macro scale. You will collaborate and communicate with a multi-disciplinary team of engineers and abuse analysts on a wide range of problems. You will bring problem-solving excellence and statistical methods to understanding emerging risks with specific focus on responsible AI, testing standards and red teaming. You will also have the opportunity to motivate impact in a diverse range of tests ranging from measuring quality, developing abuse metrics, and optimizing content moderation workflows, operational systems and abuse protections with data-motivated focus.The US base salary range for this full-time position is $150,000-$223,000 + 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• Work with large data sets, solve analysis problems and apply advanced problem-solving methods as needed. Conduct end-to-end analysis including data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.• Build and prototype analysis pipelines to provide insights. Develop understanding of Google data structures and metrics, advocating for changes where needed.• Interact cross-functionally with a variety of teams. Work with engineers to identify opportunities for, design, and assess improvements to Google products.• Make business recommendations (e.g., cost-benefit, forecasting, experiment analysis) with presentations of findings at multiple stakeholder levels through visual quantitative information displays.• Research and develop analysis, forecasting, and optimization methods to improve Google's user facing Generative AI products and internal operations (e.g., AI testing standards, adversarial analysis, quantifying and optimizing red teaming exercises, prompt analysis and evaluate data set optimization).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 .
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Average salary estimate

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$175500 / ANNUAL (est.)
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$117K
$234K

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What You Should Know About Data Scientist, Research, Responsible AI, Trust and Safety, Google

Are you ready to step into the world of responsible AI and make a significant impact? Join Google as a Data Scientist in Trust and Safety, where you will be at the forefront of making the internet a safer place for everyone. Located in the vibrant city of Washington, DC, this role calls for an analytical thinker with a Master’s degree in Statistics, Data Science, or a related field, complemented by at least five years of hands-on experience in solving complex business problems. You will dive into large datasets, utilizing tools like Python and SQL to derive valuable insights and make data-driven recommendations. Your expertise in machine learning, predictive modeling, and statistical analysis will be pivotal as you work alongside a diverse team of engineers and abuse analysts, focusing on the challenges posed by fraud and security in the digital landscape. At Google, you’ll not only apply your technical skills but also refine your problem-solving abilities, collaborating across various teams to enhance product safety and user trust. The role promises an engaging work environment, where your efforts will help shape operational standards while focusing on the ethical dimensions of AI. With a competitive salary range from $150,000 to $223,000, plus bonuses and benefits, there’s no better time to join a true leader in tech. If you’re passionate about fostering trust and safety in the digital world, we’d love to hear from you!

Frequently Asked Questions (FAQs) for Data Scientist, Research, Responsible AI, Trust and Safety Role at Google
What qualifications do you need for the Data Scientist position at Google in Trust and Safety?

To apply for the Data Scientist position at Google within the Trust and Safety team, candidates should have a Master's degree in a quantitative field such as Statistics, Data Science, Mathematics, or related areas. Additionally, previous experience of five years in solving product or business problems, or three years with a PhD, is preferred. Knowledge of tools and languages like Python, R, and SQL is essential, along with a strong understanding of machine learning and statistical analysis.

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What are the key responsibilities of a Data Scientist at Google in Trust and Safety?

As a Data Scientist in Google's Trust and Safety team, you will manage large datasets and apply advanced analytical methods to solve complex problems. Key responsibilities include conducting end-to-end analyses, building analysis pipelines, collaborating with engineering teams, and developing business recommendations based on your findings. You'll also focus on enhancing Google's Generative AI products and participating in fraud detection and risk mitigation efforts.

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What skills are preferred for the Data Scientist role at Google?

Preferred skills for the Data Scientist role in Google's Trust and Safety division include experience with machine learning techniques, proficiency in statistical data analysis, and familiarity with fraud and security in the context of Internet-related products. Excellent communication abilities and collaborative skills are also vital to effectively interact with a multi-disciplinary team and stakeholders.

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How does the Trust and Safety team at Google leverage data science?

The Trust and Safety team at Google leverages data science to inform decision-making, motivate operational excellence, and enhance user trust. Data Scientists evaluate and analyze the effectiveness of products, develop metrics to assess abuse and risk, and contribute to the testing and improvement of AI standards. This involves rigorous analysis, statistical methods, and building predictive models to enhance operational efficiency and user safety.

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What is the salary range for a Data Scientist in Trust and Safety at Google?

The salary range for a Data Scientist in Google’s Trust and Safety team varies from $150,000 to $223,000, depending on experience and location. This range is complemented by bonuses, equity, and comprehensive benefits. It’s important to discuss specific salary offerings with the recruiter during the hiring process, as these can vary based on individual qualifications and other factors.

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Common Interview Questions for Data Scientist, Research, Responsible AI, Trust and Safety
Can you describe a complex data analysis project you handled previously?

When answering this question, detail the project scope, your specific role, and the tools you utilized. Highlight how your analysis contributed to business decisions, emphasizing any outcomes or insights you provided that drove change within the organization. Show your problem-solving skills and the analytical methods you employed.

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How do you ensure the accuracy and integrity of your data analysis?

Discuss your processes for data validation, cleaning, and verifying outputs throughout your analysis. Provide examples of how you have managed data quality in past projects, emphasizing tools and techniques used to mitigate errors and ensure reliability.

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What statistical methods are you most comfortable with, and how have you used them?

Outline the statistical methods you are proficient in, such as regression analysis, hypothesis testing, or predictive modeling. Provide specific examples of how you applied these methods to real-world problems, demonstrating your knowledge and expertise in delivering insights.

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How do you approach problem-solving in data science?

Explain your approach to problem-solving by discussing steps you take, such as defining the problem, analyzing data, developing hypotheses, and validating results. Provide insights into how you collaborate with teams and communicate your findings to ensure everyone is aligned.

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Can you discuss your experience with machine learning?

Elaborate on your experience with machine learning, detailing the algorithms you have used, projects you've completed, and the impact your models had on outcomes. Focus on your ability to prepare datasets, train models, and assess their performance.

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How do you stay updated with the latest trends and technologies in data science?

Discuss your commitment to staying informed about data science, such as attending workshops, webinars, or following industry publications. Mention any communities or networks you are part of that allow you to share knowledge and learn from peers.

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What is your experience with data visualization tools?

Talk about the data visualization tools you have used, such as Tableau, Power BI, or Python libraries. Provide examples of how you've created visual representations of data to enhance storytelling and make findings accessible to non-technical stakeholders.

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How would you handle conflicting priorities in projects?

Explain your strategy for managing conflicting priorities by discussing how you assess project importance, communicate with stakeholders, and reprioritize tasks effectively. Highlight your organizational skills and ability to keep key projects on track.

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Describe a time when you had to explain complex data findings to a non-technical audience.

Share an example where you successfully translated complex data insights into understandable terms for a non-technical audience. Discuss how you focused on key messages and storytelling techniques to ensure clarity and engagement.

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What challenges do you think the Trust and Safety team at Google faced, and how can data science address them?

Discuss the challenges of online safety and abuse prevention, mentioning issues like fraud detection and user trust. Propose how data science can analyze patterns, predict potential risks, and provide actionable insights to enhance Google’s Trust and Safety measures.

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CULTURE VALUES
Inclusive & Diverse
Rise from Within
Mission Driven
Diversity of Opinions
Work/Life Harmony
Take Risks
Collaboration over Competition
Growth & Learning
Transparent & Candid
Customer-Centric
Social Impact Driven
Rapid Growth
Passion for Exploration
Dare to be Different
Reward & Recognition
Friends Outside of Work
BENEFITS & PERKS
Medical Insurance
Dental Insurance
Vision Insurance
Mental Health Resources
Life insurance
Disability Insurance
Health Savings Account (HSA)
Flexible Spending Account (FSA)
Conferences Stipend
Bias Training
Employee Resource Groups
401K Matching
Paternity Leave
Maternity Leave
Some Meals Provided
Social Gatherings
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Full-time, on-site
DATE POSTED
December 17, 2024

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