1 Π΄Π΅Π½Ρ Π½Π°Π·Π°Π΄
Staff Data Scientist (AI)
152Β 000 - 282Β 000$
ΠΡΡΡ & Π‘ΠΎΠΏΡΠΎΠ²ΠΎΠ΄
ΠΠ»Ρ ΠΌΡΡΡΠ° Ρ ΡΡΠΎΠΉ Π²Π°ΠΊΠ°Π½ΡΠΈΠ΅ΠΉ Π½ΡΠΆΠ΅Π½ Plus
ΠΠΏΠΈΡΠ°Π½ΠΈΠ΅ Π²Π°ΠΊΠ°Π½ΡΠΈΠΈ
Π’Π΅ΠΊΡΡ:
TL;DR
Staff Data Scientist (AI): Building causal measurement, LTV prediction, personalization, and real-time machine learning systems for marketplace optimization with an accent on incrementality, experimentation, and revenue-driven decisioning. Focus on designing causal inference frameworks, deploying production ML models, and shaping scalable data and AI platform architecture.
Location: Remote, based in the United States
Salary: $152,000β$282,000 per year, plus equity
Company
develops financial products and tools that help people make informed financial decisions.
What you will do
- Design and implement causal inference frameworks for personalization, marketing, and lifecycle interventions.
- Establish methodologies for incrementality, experimentation, and measurement across channels and product surfaces.
- Build and scale user-level and cohort-based LTV models for real-time decisioning.
- Develop and deploy personalization and machine learning models for ranking, offer selection, content sequencing, marketplace optimization, partner routing, and budget allocation.
- Define data instrumentation, feature stores, model monitoring, and real-time inference architecture with Data Engineering and Platform teams.
- Set technical standards, mentor data scientists, and communicate analytical trade-offs to Product, Engineering, Marketing, Finance, and executive stakeholders.
Requirements
- 8+ years of experience in applied machine learning, causal inference, experimentation, or related quantitative fields.
- Deep expertise in causal inference, including uplift modeling, doubly robust learners, instrumental variables, difference-in-differences, synthetic control, and Bayesian time series.
- Experience building and operationalizing LTV models for real-time or near-real-time applications.
- Strong production ML and software engineering skills with Python, PySpark, advanced SQL, scikit-learn, LightGBM, or XGBoost.
- Experience with distributed systems, modern data platforms, experimentation frameworks, A/B testing, statistical diagnostics, version control, and ML lifecycle tools.
- Ability to influence cross-functional and executive stakeholders and operate strategically in ambiguous, high-impact problem spaces.
Nice to have
- Experience with marketplace optimization, ranking systems, or auction-based environments.
- Familiarity with contextual bandits, reinforcement learning, or sequential decision-making.
- Knowledge of streaming architectures, orchestration tools, and feature stores.
- Domain experience in fintech, marketplaces, growth, or performance marketing.
Culture & Benefits
- Flexible remote and in-office work environment with support for employee well-being and development.
- Medical, dental, and vision coverage, mental health support, and flexible paid time off.
- New parent leave, paid sabbatical after five years, and quarterly volunteer time off with company matching.
- Wellness, phone, Wi-Fi, home office equipment, and coworking stipends; Wi-Fi and home office stipends apply to remote employees.
- 401(k) with a 4% company match, FSA and HSA plans, disability and life insurance, and access to a Certified Financial Planner.
- Employee resource groups, hackathons, team events, and company-wide initiatives.
ΠΡΠ΄ΡΡΠ΅ ΠΎΡΡΠΎΡΠΎΠΆΠ½Ρ: Π΅ΡΠ»ΠΈ ΡΠ°Π±ΠΎΡΠΎΠ΄Π°ΡΠ΅Π»Ρ ΠΏΡΠΎΡΠΈΡ Π²ΠΎΠΉΡΠΈ Π² ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡ iCloud/Google, ΠΏΡΠΈΡΠ»Π°ΡΡ ΠΊΠΎΠ΄/ΠΏΠ°ΡΠΎΠ»Ρ, Π·Π°ΠΏΡΡΡΠΈΡΡ ΠΊΠΎΠ΄/ΠΠ, Π½Π΅ Π΄Π΅Π»Π°ΠΉΡΠ΅ ΡΡΠΎΠ³ΠΎ - ΡΡΠΎ ΠΌΠΎΡΠ΅Π½Π½ΠΈΠΊΠΈ. ΠΠ±ΡΠ·Π°ΡΠ΅Π»ΡΠ½ΠΎ ΠΆΠΌΠΈΡΠ΅ "ΠΠΎΠΆΠ°Π»ΠΎΠ²Π°ΡΡΡΡ" ΠΈΠ»ΠΈ ΠΏΠΈΡΠΈΡΠ΅ Π² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΡ. ΠΠΎΠ΄ΡΠΎΠ±Π½Π΅Π΅ Π² Π³Π°ΠΉΠ΄Π΅ β
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