{"id":3246108,"url":"https://alion.io/job/avrora-digital-ml-inzhener-classic-ml","title":"ML-инженер (Classic ML)","company":{"id":4424741,"name":"Avrora Digital","domain":"avrora-digital.ru","url":"https://alion.io/company/avroradigital","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"HeadHunter","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"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":26000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":11},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CatBoost","optional":false},{"name":"Git","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"XGBoost","optional":false},{"name":"MLFlow","optional":true},{"name":"pySpark","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-25T19:04:31Z","employer_posted_date":"2026-10-25","last_verified_at":"2026-11-01T19:41:34Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"Data Scientist (Classic ML)\n Мы используем машинное обучение в ключевых бизнес-процессах: скоринг и оценка рисков, прогнозирование спроса, отток и удержание клиентов, антифрод. Модели работают в продакшене и напрямую влияют на деньги, и мы ищем Data Scientist, который возьмёт их на себя от гипотезы до внедрения.\n Если ты умеешь не только построить модель на табличных данных, но и показать её эффект для бизнеса - ждём тебя.\n Обязанности\nРазрабатывать модели на табличных данных: скоринг, отток, LTV, uplift, антифрод;\nСтроить модели прогнозирования временных рядов: спрос, нагрузка, финансовые показатели;\nПроводить EDA, генерировать и отбирать признаки (feature engineering);\nВыводить модели в production и поддерживать их: мониторинг качества, дрейф данных, переобучение;\nОценивать качество моделей (ROC-AUC, Gini, PSI) и эффект внедрения через A/B-тесты;\nРаботать в связке с бизнесом: формулировать задачи, проверять гипотезы, презентовать результаты;\nСледить за новыми подходами и внедрять то, что работает.\n Требования\nУверенный Python: pandas, numpy, scikit-learn;\nГлубокое знание градиентного бустинга (CatBoost, LightGBM, XGBoost) и классических алгоритмов ML;\nЗнание математической статистики и теории вероятностей;\nОпыт проведения A/B-тестов и проверки статистических гипотез;\nПродвинутый SQL;\nОпыт вывода моделей в продакшен;\nGit, культура код-ревью;\nОпыт от 3 лет.\n Будет плюсом\nОпыт в финтехе, банке или ритейле: кредитные риски, антифрод, ценообразование;\nОпыт с временными рядами: Prophet, ETNA, sktime;\nSpark / PySpark, Airflow, MLflow;\nОпыт uplift-моделирования и causal inference;\nАнглийский для чтения документации и статей;\nПрофильное техническое образование.\n Условия\nУдалённо или гибрид;\nДМС;\nБонусы и премии по результатам;\nОформление по ТК РФ;\nКомпенсация спорта;\nОбучение за счёт компании, участие в 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