12 дней назад
Data Scientist – Time Series Forecasting
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Data Scientist – Time Series Forecasting (Python/R, SQL, ARIMA, XGBoost, LightGBM): Building and scaling forecasting models for demand, sales, and supply chain metrics with an accent on historical data analysis, statistical modeling, and machine learning. Focus on deploying models to production, tracking forecast performance, and translating data insights into business plans.
Location: Hyderabad, India; 6 days per week working from the office
Company
operates in the pharmacy and retail sector.
What you will do
- Build and scale time-series forecasting models for demand, sales, and supply chain metrics.
- Clean, process, and analyse large historical datasets from multiple sources.
- Test statistical and machine learning algorithms to improve model accuracy.
- Deploy forecasting models into production and monitor their performance over time.
- Collaborate with product and business teams to turn data insights into actionable plans.
Requirements
- 2–5 years of hands-on data science experience focused on forecasting.
- Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, or a related field.
- Strong programming skills in Python or R and SQL for data extraction.
- Experience with ARIMA, Exponential Smoothing, Prophet, XGBoost, and LightGBM.
- Proficiency in pandas, scikit-learn, statsmodels, and MLflow or similar model-tracking tools.
- Good problem-solving skills and the ability to explain complex data to non-technical teams.
Nice to have
- Deep learning experience with LSTM or RNN models.
Culture & Benefits
- Onsite work schedule of six days per week.
Hiring process
- Walk-in interview drive on 5 September 2026.
Будьте осторожны: если работодатель просит войти в их систему, используя iCloud/Google, прислать код/пароль, запустить код/ПО, не делайте этого - это мошенники. Обязательно жмите "Пожаловаться" или пишите в поддержку. Подробнее в гайде →