- Страна
- США
- Зарплата
- 187 000 $ – 220 000 $
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Senior Data Scientist, ML (Incentives)
Отличная вакансия в топовой финтех-компании с прозрачной вилкой зарплаты, сильным социальным пакетом и возможностью влиять на продукт мирового уровня. Высокий балл за репутацию бренда и сложность инженерных задач.
Сложность вакансии
Высокая сложность обусловлена требованием глубоких знаний в области причинно-следственного вывода (causal inference) и моделирования аплифта (uplift modeling), а также необходимостью иметь более 5 лет опыта работы с ML в продакшене. Роль предполагает высокую ответственность за бюджеты и стратегию роста компании.
Анализ зарплаты
Предложенная зарплата в $187k–$220k для Menlo Park полностью соответствует рыночным стандартам для уровня Senior Data Scientist в Tier-1 компаниях Кремниевой долины. С учетом бонусов и опционов совокупный доход может значительно превышать средние показатели по рынку.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Data Scientist, ML (Incentives) position at Robinhood. With over five years of experience in deploying machine learning models in production and a deep background in causal inference and uplift modeling, I am excited by the opportunity to optimize Robinhood’s incentive systems. My experience in building end-to-end ML pipelines and designing complex experimentation frameworks aligns perfectly with your team's mission to drive sustainable growth.
In my previous roles, I have successfully translated ambiguous business goals into scalable algorithmic solutions, specifically in the domains of growth and personalization. I am particularly drawn to Robinhood’s mission of democratizing finance and am eager to apply my expertise in Python, SQL, and causal ML to maximize the ROI of your promotional programs. I look forward to the possibility of contributing to your high-performing team in Menlo Park and helping shape the long-term vision for growth modeling.
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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 Incentives Data Science team sits at the intersection of Product, Marketing, Finance and Machine Learning. Our mission is to enable sustainable, data-driven growth by building modeling, measurement, and optimization systems that drive activation, retention, and revenue at scale. We partner closely with cross-functional teams across Robinhood to design, evaluate, and operationalize incentive programs that efficiently acquire, activate, and retain customers.
As a Senior Data Scientist, ML, you will lead the end-to-end design, optimization, and evolution of Robinhood’s incentive systems. You’ll build predictive and causal ML models, design experimentation frameworks, and develop decisioning and allocation algorithms that directly influence how millions of users engage with Robinhood. This is a rare opportunity to own highly impactful ML systems while shaping incentive strategy at company scale!
This role is based in our Menlo Park, CA office, 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
- Build, deploy, and iterate on predictive and causal models for incentive targeting
- Design and evaluate experiments to measure incremental impact, payback, and ROI of promotional programs
- Partner cross-functionally with Product, Finance, Marketing, and Engineering to inform scalable incentive strategies
- Design and optimize incentive allocation algorithms under budget and policy constraints to maximize incremental impact
- Monitor production systems, analyze user behavior, and propose algorithmic and policy improvements
- Influence the long-term vision for growth modeling at Robinhood, including personalization and cross-sell optimization
What you bring
- 5+ years of experience applying ML in production, ideally in growth, incentives, marketplace, or personalization domains
- Proven track record owning models end-to-end — from design and development to deployment and iteration
- Deep expertise in predictive modeling, uplift modeling, causal inference, and experimentation design
- Strong technical skills in Python, SQL, and ML frameworks (e.g., scikit-learn, XGBoost, PyTorch or TensorFlow)
- Experience working with Airflow and production pipelines, as well as tools for causal ML and experimentation
- Excellent product intuition and ability to translate ambiguous goals into measurable algorithmic solutions
- Strong communication and stakeholder management skills, with the ability to influence across teams
- A collaborative and growth-minded approach, with strong technical leadership and a consistent focus on impact and ROI
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
- Python
- PyTorch
- Machine Learning
- SQL
- Scikit-learn
- Airflow
- TensorFlow
- XGBoost
- Causal Inference
- Uplift Modeling
- Experimentation Design
Возможные вопросы на собеседовании
Роль требует оптимизации стимулов, где важно понимать не просто корреляцию, а причинно-следственную связь.
Расскажите о вашем опыте применения Causal Inference или Uplift Modeling для решения бизнес-задач. С какими основными трудностями вы столкнулись?
Вакансия подразумевает работу с бюджетами и максимизацию ROI.
Как бы вы спроектировали алгоритм распределения бонусов между пользователями при ограниченном маркетинговом бюджете?
Работа в Robinhood требует тесного взаимодействия с продуктовыми и финансовыми командами.
Опишите случай, когда вам пришлось объяснять сложную ML-модель нетехническим стейкхолдерам. Как вы убедили их в правильности вашего подхода?
Упоминается использование Airflow и работа с продакшен-пайплайнами.
Каков ваш опыт развертывания и мониторинга ML-моделей в реальном времени? Как вы обрабатываете дрейф данных (data drift) в моделях таргетинга?
Эксперименты — ключ к измерению инкрементального эффекта.
Как вы подходите к дизайну экспериментов, когда стандартное A/B тестирование невозможно или неэтично?
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- Страна
- США
- Зарплата
- 187 000 $ – 220 000 $