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Staff Data Scientist (AI)

152Β 000 - 282Β 000$
Π€ΠΎΡ€ΠΌΠ°Ρ‚ Ρ€Π°Π±ΠΎΡ‚Ρ‹
remote (Ρ‚ΠΎΠ»ΡŒΠΊΠΎ USA)
Π’ΠΈΠΏ Ρ€Π°Π±ΠΎΡ‚Ρ‹
fulltime
Π“Ρ€Π΅ΠΉΠ΄
senior
Английский
b2
Π‘Ρ‚Ρ€Π°Π½Π°
US
Вакансия ΠΈΠ· списка Hirify.GlobalВакансия ΠΈΠ· Hirify Global, списка ΠΌΠ΅ΠΆΠ΄ΡƒΠ½Π°Ρ€ΠΎΠ΄Π½Ρ‹Ρ… tech-ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ
Для мэтча ΠΈ ΠΎΡ‚ΠΊΠ»ΠΈΠΊΠ° Π½ΡƒΠΆΠ΅Π½ Plus

ΠœΡΡ‚Ρ‡ & Π‘ΠΎΠΏΡ€ΠΎΠ²ΠΎΠ΄

Для мэтча с этой вакансиСй Π½ΡƒΠΆΠ΅Π½ Plus

ОписаниС вакансии

ВСкст:
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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: $152,000–$282,000 per year, plus equity

Company

hirify.global 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.

Π‘ΡƒΠ΄ΡŒΡ‚Π΅ остороТны: Ссли Ρ€Π°Π±ΠΎΡ‚ΠΎΠ΄Π°Ρ‚Π΅Π»ΡŒ просит Π²ΠΎΠΉΡ‚ΠΈ Π² ΠΈΡ… систСму, ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡ iCloud/Google, ΠΏΡ€ΠΈΡΠ»Π°Ρ‚ΡŒ ΠΊΠΎΠ΄/ΠΏΠ°Ρ€ΠΎΠ»ΡŒ, Π·Π°ΠΏΡƒΡΡ‚ΠΈΡ‚ΡŒ ΠΊΠΎΠ΄/ПО, Π½Π΅ Π΄Π΅Π»Π°ΠΉΡ‚Π΅ этого - это мошСнники. ΠžΠ±ΡΠ·Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎ ΠΆΠΌΠΈΡ‚Π΅ "ΠŸΠΎΠΆΠ°Π»ΠΎΠ²Π°Ρ‚ΡŒΡΡ" ΠΈΠ»ΠΈ ΠΏΠΈΡˆΠΈΡ‚Π΅ Π² ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΡƒ. ΠŸΠΎΠ΄Ρ€ΠΎΠ±Π½Π΅Π΅ Π² Π³Π°ΠΉΠ΄Π΅ β†’