{"id":3196948,"url":"https://alion.io/job/uzum-senior-data-scientist-ml-evaluation-metrics","title":"Senior Data Scientist (ML Evaluation & Metrics)","company":{"id":2597605,"name":"Uzum","domain":"uzum.com","url":"https://alion.io/company/uzum-com","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"HeadHunter","truth_index":{"grade":"A","score":89,"open_postings":24,"ghost_share":0.125,"stale_share":0,"repost_share":0.208,"time_to_fill_p50_days":30,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Moscow, Russia"],"countries":["RU"],"hiring_countries":["RU","BY","KZ","KG","AM"],"hiring_countries_total":5,"salary":null,"salary_estimate":{"min_usd":25000,"max_usd":53000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"SciPy","optional":false},{"name":"ClearML","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"Label Studio","optional":true},{"name":"LLM","optional":true},{"name":"MLFlow","optional":true}],"status":"live","first_seen_at":"2026-10-23T08:41:56Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-29T13:57:41Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Senior Data Scientist (ML Evaluation & Metrics)\n Чем предстоит заниматься:\n Проектировать методологии оценки качества ML-моделей и рекомендательных механик: офлайн- и онлайн-метрики, их связь с бизнес-метриками\n Переводить потребности бизнеса в DS-постановки, формулировать гипотезы, целевые и guardrail-метрики, ограничения, критерии успеха\n Разрабатывать методологию разметки: ТЗ и инструкции для разметчиков, правила для пограничных случаев, golden sets, контроль согласованности, аудит качества разметки\n Готовить ТЗ на сбор и упаковку наборов данных\n Разрабатывать дизайны и анализировать результаты тестов ML решений в продукте\n Формулировать и имплементировать методологии оценки качества данных\n Вести исследовательскую аналитику, находить инсайты в данных и в поведении ML систем.\n Отвечать за отчётность и логирование ML-метрик. Определяя формат релизных отчётов, обязательный набор метрик, сравнение с baseline\n Определять дизайн оценки качества. Формировать драфт задач для ML инженеров, описывать, что и как должно быть реализовано, чтобы качество можно было корректно измерить.\n Для нас важно:\n От 3 лет коммерческого опыта в Data Science или ML-аналитике, включая реальный опыт оценки моделей, работающих в проде\n Глубокое понимание метрик ML (классификация, детекция, сегментация, ранжирование)\n Уверенные теорвер и матстат: проверка гипотез, доверительные интервалы, бутстрап, мощность теста, методы снижения дисперсии\n Классический ML на табличных данных (бустинги, линейные модели): умение анализировать поведение модели, её ошибки, калибровку, важность признаков\n Python (pandas, numpy, scipy, sklearn, Jupyter)\n Умение писать: компактные и внятные отчёты с выводами, ТЗ, инструкции, которые понимают с первого раза\n Умение задавать правильные вопросы и докапываться до сути постановки\n Крайне желательно - опыт оценки рекомендательных систем\n Высшее техническое или математическое образование\n Будет плюсом:\n Опыт в модерации 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