fintechpythonsqlfraud detectionmachine learninga b testingairflowtensorflowstartupfeature engineering
Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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В роли есть четкая ответственность за промышленное ML и эксперименты в растущем платежном продукте, но стартап-среда и широкий технический лидерский мандат добавляют неопределенности в реализации.
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Риски стартапаЗона ответственностиАктуальные технологииПерспективный финтехАкции и развитие
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Текст:
Hirify AI/Оригинал
TL;DR
Data Scientist (Fintech): Building and deploying machine learning models for payment optimization with an accent on conversion improvement, decline reduction, fraud detection, and real-time payment flows. Focus on designing experimentation frameworks, optimizing features and hyperparameters, and validating model impact across merchants and payment providers.
Location: Hybrid in San Francisco or New York, US; remote work from anywhere is also listed as a benefit.
Company
hirify.global is a rapidly growing fintech startup building a global payments and next-generation commerce platform for merchants across LATAM.
What you will do
Own the end-to-end modeling lifecycle for payment optimization, including transaction analysis, strategy definition, feature generation, model training, tuning, and deployment.
Build an experimentation framework to measure model performance from early signals through month-over-month results.
Develop models that improve conversion, reduce payment declines, and detect fraud in real-time payment flows.
Collaborate with project leads, data engineering, BI, systems, and infrastructure teams on customer-facing AI and modeling solutions.
Guide data processing pipeline development and use rapid model iteration to address merchant data and business goals.
Requirements
5+ years of experience applying machine learning to real production problems, ideally in payments, fintech, fraud, or other high-volume transactional environments.
Ability to define required data, select modeling approaches, and establish how model effectiveness will be validated.
Hands-on experience with feature generation, model selection, training, and hyperparameter optimization.
Experience designing and running experiments, including A/B testing and experimentation platforms.
Proficiency in Python and SQL, with the ability to query large datasets using tools such as Snowflake.
Familiarity with TensorFlow, Airflow, model management, and model rollout practices.
Ability to guide technical infrastructure teams in implementing efficient data science and ML training pipelines and systems.
Self-directed working style and clear communication with technical and non-technical audiences, including leadership.
Nice to have
Payments, fintech, fraud, or high-volume transactional experience is preferred.
People leadership may become part of the role if aligned with the candidate’s skills.
Culture & Benefits
Multicultural, multidisciplinary, diverse, and dynamic team distributed throughout LATAM.
Startup environment focused on dynamism, agility, constant innovation, and regional impact.
Hybrid workplace with remote work from anywhere listed among the benefits.
Vacations, additional PTO, and financial support for internet and mobile service.
Stock options, growth opportunities, and a defined career path.
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