Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Роль четко определена с ясными обязанностями и хорошей зарплатой для младшей позиции, но акцент на конкретных инструментах и методологиях может ограничить возможности для более широкого обучения.
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Четкое определение ролиХорошая зарплатаПродуктовая компанияОграниченные возможности обучения
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Junior Data Scientist (Python/SQL): Building and maintaining data science models and production pipelines for ecommerce marketing measurement with an accent on attribution, forecasting, incrementality testing, and model backtesting. Focus on systematically debugging unfamiliar codebases, reviewing AI-assisted production code, validating AWS pipelines, and explaining methodology and findings to clients and colleagues.
Location: London, United Kingdom
Company
hirify.global builds marketing measurement products for ecommerce brands, including attribution, marketing mix modelling, incrementality testing, and brand impact measurement.
What you will do
Own moderately difficult tickets from analytical investigations and model backtesting to production bug fixes and pipeline work.
Systematically debug partly unfamiliar codebases using AI tooling to accelerate investigations.
Write and review production Python and SQL while avoiding technical debt.
Use QA automation and AWS data science pipeline tooling effectively.
Explain methodology and findings to clients and colleagues.
Clarify acceptance criteria and become an internal data science contact for owned areas.
Requirements
1–2 years of commercial data science experience or a strong placement/internship record alongside a quantitative degree.
Strong Python and SQL skills with experience collaborating on shared code.
Deep knowledge of several machine learning algorithms and when to apply them.
Ability to systematically debug unfamiliar code and independently complete scoped work.
Strong AI fluency, including reviewing AI output for accuracy and brevity.
Clear communication, attention to detail, critical thinking, and initiative.
Nice to have
Experience with AWS or comparable cloud tooling.
Familiarity with automated QA tooling and test coverage practices.
Experience with marketing, ecommerce, or advertising data.
Exposure to Bayesian methods, marketing mix modelling, or experimental design.
Culture & Benefits
Published six-level Data Science Career Development Framework with explicit progression criteria.
Regular code review and methodology critique focused on model quality.
Supportive team structure with progressively increasing ownership.
Direct impact on budget decisions through models used by recognizable brands.
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