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Senior Data Scientist

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Salary
USD 178,500–178,500 / yr
Employment
Full Time
Experience
Senior
Published
Apply before
9 Nov 2026
Listing views
91
Application actions
6
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AI Summary

The role, at a glance.

DuckDuckGo is hiring a Senior Data Scientist to lead high-complexity, end-to-end data science initiatives across search, AI, product growth, marketing measurement, and revenue optimization. The role combines statistical modeling, experiment design, Bayesian campaign analysis, production machine learning, NLP/LLM applications, and monitoring for privacy-constrained data. Candidates need 7+ years of experience, strong Python and advanced SQL skills, and the ability to independently scope, build, deploy, and evaluate impactful solutions. This is a remote, full-time role available in the explicitly listed US and European countries, with periodic travel for company and team events. The position emphasizes rigorous quantitative judgment, ownership, and close collaboration with product teams.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a senior, highly autonomous role requiring proven delivery of complex data science and machine-learning work from ambiguous scoping through production. The combination of sparse privacy-preserving data, experimentation, Bayesian methods, NLP/LLMs, and cross-functional product impact makes the technical and judgment requirements demanding.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate
$178,500
US market range$150k–$210k
AI insightThe posting explicitly discloses annual compensation of $178,500 USD, so the job median is $178,500. For the US market, a reasonable estimated base-salary range for a senior data scientist with production ML, experimentation, NLP/LLM, and analytics responsibilities is approximately $150,000–$210,000 annually; the disclosed salary falls within this range. Stock options are additional compensation and are not included in the cash salary figures.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe a complex data science project you led from ambiguous problem definition through production deployment.

I would explain how I aligned stakeholders on a measurable objective, assessed available data and constraints, selected an appropriately simple and testable modeling approach, and established offline and production success metrics. I would then describe deployment, monitoring, iteration, and the business or product outcome, including lessons learned.

How would you measure the impact of a marketing campaign when user-level data is sparse or privacy constrained?

I would first define the causal question and available aggregation level, then use approaches such as Bayesian structural time series, geo experiments, matched-market analysis, or hierarchical models. I would communicate uncertainty clearly, validate against holdout periods where possible, and combine the results with operational context rather than overclaiming precision.

How do you design an experiment for a new AI-assisted search-answer trigger?

I would define primary quality and user-value metrics along with guardrails such as abandonment, latency, complaint rates, and privacy requirements. I would choose an eligible population, power the experiment for meaningful effects, randomize at an appropriate unit, pre-specify analysis, and segment results to identify where the trigger improves or degrades the experience.

What practices do you use to make an ML model reliable in production?

I use reproducible training pipelines, versioned data and models, automated validation, clear serving contracts, and staged releases. I also implement monitoring for data drift, feature health, model performance, latency, and business metrics, with documented rollback criteria and ownership for incident response.

How would you use an LLM-as-judge while maintaining a rigorous evaluation process?

I would create a carefully defined rubric and benchmark it against high-quality human labels across representative and adversarial examples. I would measure agreement, inspect systematic failure modes, use calibration or multiple judging passes when appropriate, and retain human review for consequential decisions or areas where the judge is unreliable.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

Who We Are

Hi, we’re DuckDuckGo, the online protection company and remote-first team of 300+ on a mission to raise the standard of trust online. Founded in 2008 and profitable since 2014, annual revenue now exceeds $100m USD and millions use our browser on on Mac, Windows, iOS, and Android, our search engine, and the DuckDuckGo subscription. We also offer private, useful, and optional AI, including Duck.ai, which lets you chat privately with ChatGPT, Claude, and other AIs, all in one place. Our culture of trust, inclusivity, and empowered project management underpins everything we do, where each team member takes full ownership of their projects, from scoping and execution to postmortem. If you’re seeking end-to-end ownership of your work, you’ve come to the right place!

Your Team and Role

Working on the Data Science Functional Team, you’ll drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, and classifier implementation across our search engine, instant answers, AI, and revenue optimization, and work on related projects. Recent projects include:

  • Defining core growth metrics for our products and building the infrastructure to monitor trends and anomalies

  • Measuring the impact of major marketing campaigns for Duck.ai, translating full-funnel data into actionable decisions

  • Building monitoring pipelines to detect unexpected shifts in search traffic patterns faster and more reliably

  • Expanding evaluation datasets for page context understanding across complex scenarios like multi-tab browsing and content reattachment

  • Improving the trigger systems that determine when AI-assisted answers surface in DuckDuckGo Search, balancing quality and our privacy-first approach

Our data meets rigorous privacy standards and is necessarily sparse, so you’ll need to think creatively to surface actionable insights in an evidence-first culture where quantitative rigor is the baseline, not the exception.

About You

  • You have 7+ years of experience in Data Science and a track record of leading high-complexity projects from scoping to production with minimal direction.

  • You can do 2 or more of the following:

  • Deploy and iterate on ML in production environments

  • Develop and deploy NLP solutions

  • Apply knowledge of LLMs including fine-tuning, LLM-as-judge, and AI-assisted development

  • Conduct marketing campaign analysis using Bayesian methods

  • You write production-grade code in Python or a comparable high-level language.

  • You have advanced SQL and query optimization skills, familiarity with columnar databases like BigQuery, Clickhouse, Redshift, or Druid; experience with a BI tool like Tableau or PowerBI is a plus.

  • You can design, implement, and analyze experiments across product surfaces, and are comfortable deriving insights from sparse or privacy-constrained data.

  • You collaborate effectively with product teams and contribute to overall team throughput.

Compensation

$178,500 USD annually and stock options. Compensation is transparent across the organization, and all team members within the same professional level and global region receive the same compensation.

Eligibility for company-sponsored health benefits is limited to team members based in the United States. This program does not extend to team members located in other countries, such as Canada or the UK.

Our Team Member Support Guide explains how we prioritize your wellbeing including paid parental leave, office setup, and co-working allowances.

Hiring Process

Hiring works best when it’s a two-way street. Learn how we help you get to know DuckDuckGo, envision your future role here, and find out more about how we hire.

Diversity, Equity, and Inclusion

DuckDuckGo provides equal work opportunities to all team members and applicants, and it prohibits discrimination and harassment of any type on the basis of race, color, ethnicity, caste, religion, age, sex (including pregnancy), national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by our policies or federal, state, or local laws.

We want to ensure that our hiring process is accessible. If you need reasonable accommodation for any part of the application process because of a medical condition or disability, please send an email to careers@duckduckgo.com to let us know the nature of your request.

Please note that:

  • You’ll be required to attend meetings on camera via video conferencing

  • Expect to travel at least two times a year: once for our all-hands meetup and again for a team retreat (each around 4-5 days). While extenuating circumstances may impact attendance, everyone is strongly encouraged to attend.

  • While we offer a flexible work arrangement with no core hours, expect an average full-time commitment of 40 hours per week.

  • A successful candidate must pass a background check as a condition of joining the team.

  • By applying for this role, you confirm that all information submitted is accurate and complete. You further acknowledge that providing false or fraudulent information during the application process is cause for denial of an offer, revocation of any existing offer, or other adverse action, up to and including termination after the start of your commencement of work.

Disclosure Statement: Use of AI in Hiring Process

As part of our commitment to enhancing our recruitment process, we utilize artificial intelligence (AI) technology to assist in reviewing and summarizing job applications and test projects, including those tools integrated into our recruitment vendor platforms. We use AI to flag potentially fraudulent applications, analyze and summarize applicants’ experience, interviews, and project performance, and help streamline our selection process.

Key Principles:

  • Data Privacy: All information provided in your application will be handled in accordance with our Recruiting Privacy Policy. We ensure that your personal information is protected and used solely for recruitment purposes.

  • Human Oversight and Accountability: The AI technology is designed to support our hiring team by providing insights and summaries of applications and evaluations of test projects against scoring rubrics. All final evaluations and hiring decisions, however, will be made by our hiring team, who will consider the AI’s input alongside other factors.

  • Transparency: We believe in transparency regarding our hiring practices. If you have any questions about how AI is used in our recruitment process, please feel free to reach out to us.

By submitting your application, you acknowledge and consent to the use of AI technology in our review process. If you would like to request an alternative selection process, please contact us as at careers@duckduckgo.com. Thank you for your interest in joining DuckDuckGo!

#LI-DNI

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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