- Страна
- США
- Зарплата
- 187 000 $ – 220 000 $
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Senior Data Scientist, Fraud
Отличная вакансия в топовой финтех-компании с конкурентной зарплатой, сильным социальным пакетом и возможностью работать над критически важными задачами. Высокий балл за бренд работодателя и прозрачную систему вознаграждения.
Сложность вакансии
Высокая сложность обусловлена строгими требованиями к опыту (5+ лет), необходимостью глубоких знаний в специфической области борьбы с мошенничеством и высокими ожиданиями от производительности в Robinhood. Роль требует не только технических навыков, но и способности влиять на архитектуру систем и бизнес-политики.
Анализ зарплаты
Предложенный диапазон $187k–$220k для Menlo Park полностью соответствует рыночным ожиданиям для позиции Senior уровня в топовых технологических компаниях Кремниевой долины. Верхняя граница диапазона даже несколько превышает медиану, учитывая дополнительные бонусы и опционы.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Data Scientist, Fraud position at Robinhood. With over five years of experience in applied machine learning and a deep focus on risk mitigation, I have consistently developed models that balance high precision with real-time performance. My background in building robust data pipelines and leveraging frameworks like XGBoost and TensorFlow aligns perfectly with your mission to safeguard the platform against emerging fraud vectors.
At my previous roles, I have successfully led initiatives to reduce financial loss by implementing anomaly detection systems and optimizing A/B testing strategies. I am particularly drawn to Robinhood’s fast-paced, high-impact environment and your commitment to democratizing finance. I am eager to bring my technical expertise in Python and SQL, along with my proactive approach to navigating ambiguity, to help strengthen the integrity of Robinhood’s financial ecosystem.
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Описание вакансии
Join us in building the future of finance.
Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.
About the team + role
We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.
The Fraud Data Science team safeguards Robinhood and its customers by detecting and preventing fraud and abuse across our platform. We leverage machine learning and analytics to combat malicious behavior in real time, supporting a safe and trusted experience for all users. Our work has direct impact on customer security, company risk posture, and regulatory compliance.
As a Senior Data Scientist on the Fraud team, you will own the design and deployment of ML solutions that proactively surface suspicious activity, reduce financial loss, and improve fraud detection precision. You’ll collaborate closely with engineering, product, risk, and compliance partners to influence system architecture, shape policy through data, and enhance the safety and integrity of our platform.
This role is based in our Menlo Park office(s), with in-person attendance expected at least 3 days per week.
At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.
What you’ll do
- Design and deploy fraud detection models to protect Robinhood users and assets in real time
- Analyze behavioral data to uncover emerging fraud vectors and support rapid incident response
- Develop robust data pipelines and monitoring systems to ensure model accuracy and reliability
- Partner with engineering and product teams to implement safeguards and user-facing features
- Guide experimentation strategy and contribute to long-term fraud prevention roadmap
What you bring
- 5+ years of experience in data science or applied ML, with a focus on fraud detection or risk mitigation
- Advanced proficiency in Python and SQL; experience with ML frameworks like XGBoost, LightGBM, or TensorFlow
- Strong statistical acumen with experience in anomaly detection, pattern recognition, and A/B testing
- Excellent communication skills and ability to influence decision-making across technical and non-technical audiences
- A collaborative mindset and proactive approach to navigating ambiguity in fast-paced environments
What we offer
- Challenging, high-impact work to grow your career
- Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
- Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
- Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
- Employer-paid life & disability insurance, fertility benefits, and mental health benefits
- Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
- Exceptional office experience with catered meals, events, and comfortable workspaces.
In addition to the base pay range listed below, this role is also eligible for bonus opportunities + equity + benefits.
Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected base pay range for this role is based on the location where the work will be performed and is aligned to one of 3 compensation zones. For other locations not listed, compensation can be discussed with your recruiter during the interview process.
Base Pay Range:
Zone 1 (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC)
$187,000—$220,000 USD
Zone 2 (Denver, CO; Westlake, TX; Chicago, IL)
$165,000—$194,000 USD
Zone 3 (Lake Mary, FL; Clearwater, FL; Gainesville, FL)
$146,000—$172,000 USD
Click here to learn more about our Total Rewards, which vary by region and entity.
If our mission energizes you and you’re ready to build the future of finance, we look forward to seeing your application.
Robinhood provides equal opportunity for all applicants, offers reasonable accommodations upon request, and complies with applicable equal employment and privacy laws. Inclusion is built into how we hire and work—welcoming different backgrounds, perspectives, and experiences so everyone can do their best. Please review the Privacy Policy for your country of application.
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Навыки
- A/B Testing
- Python
- Machine Learning
- SQL
- TensorFlow
- Data Pipelines
- XGBoost
- LightGBM
- Anomaly Detection
- Pattern Recognition
Возможные вопросы на собеседовании
Проверка опыта работы с несбалансированными данными, что критично для задач фрода.
Как вы справляетесь с экстремальным дисбалансом классов при обучении моделей обнаружения мошенничества?
Оценка способности кандидата быстро реагировать на новые угрозы.
Опишите ваш подход к выявлению новых, ранее неизвестных векторов атак (zero-day fraud).
Проверка навыков работы в реальном времени, что указано в описании вакансии.
Какие архитектурные компромиссы вы учитываете при развертывании ML-моделей для принятия решений в режиме реального времени?
Оценка умения работать с бизнесом и продуктом.
Как вы балансируете между точностью обнаружения фрода и минимизацией ложноположительных срабатываний, которые портят пользовательский опыт?
Проверка владения инструментарием, упомянутым в вакансии.
В каких случаях вы предпочтете использование градиентного бустинга (XGBoost/LightGBM) глубокому обучению для задач риск-менеджмента?
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- Страна
- США
- Зарплата
- 187 000 $ – 220 000 $