Staff Business Data Analyst (AI/Quality Management System)
Контакты
151 000 - 204 000$
Формат работы
onsite
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
US
aipythonsqldata visualizationstatisticsrquality managementchange managementlean six sigmaroot cause analysis
Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Зарплата и рынок
Средняя
$151к-204к/год
$130к$160кмедиана$190к
По похожим ролям (49) ~ $160к/год
На СНГ рынке: $3к/мес ($2.6к - $3.3к)
84
Хорошая вакансия
развернуть
Чётко описанная senior-роль в крупной финтех-компании с хорошей оплатой и содержательной работой на стыке аналитики и AI; широкий круг ответственности и частые рабочие ревью могут сделать нагрузку высокой.
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Широкая зона ответственностиСовременный стекХорошая оплатаAI в финтехеКрупный продукт
Оценка от Hirify AI
Мэтч & Сопровод
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Staff Business Data Analyst (AI/Quality Management System): Building defect measurement, analytics, and improvement systems for a scaled contact center operation with an accent on operational datasets, AI-scored conversations, causal analysis, and customer-impact measurement. Focus on identifying verified root causes, validating quality initiatives statistically, designing human-in-the-loop AI measurement, and driving cross-functional fixes through dashboards and executive communication.
Location: San Diego, California; Mountain View, California; or Atlanta, Georgia, United States
Base pay: $151,000–$204,000 in San Diego; $169,500–$229,000 in Mountain View.
Company
hirify.global is a financial technology platform offering products including TurboTax, Credit Karma, QuickBooks, and Mailchimp.
What you will do
Design defect measurement frameworks, DPMO instrumentation, defect taxonomies, and analytical funnels for the Expert Network.
Analyze conversation-level and journey-level operational data to identify defects, quantify customer impact, and verify root causes.
Validate improvement initiatives using controlled pre/post analysis, cohort comparisons, causal methods, and outcome metrics.
Develop human-in-the-loop quality processes and validate AI-scored conversation, resolution, and customer-distress metrics against human-labeled data.
Drive the full improvement loop from defect detection and root-cause analysis through initiative delivery, adoption, and outcome validation.
Build dashboards and present findings in operational reviews, business reviews, and executive communications.
Requirements
Strong SQL skills and working fluency in Python or R for large operational datasets.
Applied statistics knowledge, including hypothesis testing, control charts, distributions, and interpretation of metric movement.
Experience creating defect- or rate-based measurement frameworks and translating process defects into customer outcomes.
Experience with contact center operations, scaled service environments, BPO or vendor partner models, and operational metrics.
Experience with dashboards, structured improvement methods such as Lean and Six Sigma, change management, and influencing matrixed teams without direct authority.
Bachelor’s degree in a quantitative field or equivalent professional experience; 7+ years of operational analytics, quality analytics, or continuous improvement experience.
Nice to have
Experience with causal inference, experimentation, A/B testing, or difference-in-differences.
Conversation Intelligence, speech or text analytics, or AI-based quality measurement experience.
Human-in-the-loop evaluation, calibration, ground-truth labeling, explainability, bias monitoring, or responsible AI experience.
Fintech, financial services, high-technology, or software industry experience.
Change management training such as Prosci/ADKAR or Kotter.
Culture & Benefits
Competitive compensation with performance-based cash bonuses, equity rewards, and benefits eligibility.
Work is performed at one of the specified United States locations.
Regular leadership operating mechanisms include daily huddles and weekly, monthly, and quarterly business reviews.
Measurement changes are supported through communication, training, adoption, and structured change management.
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