Data Scientist (ML)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
TL;DR
Data Scientist (ML): Building and deploying production machine learning models to personalize meal experiences and optimize forecasting with an accent on time-series modeling and recommender systems. Focus on owning the full lifecycle from experimentation to production monitoring and driving measurable business outcomes.
Location: Must be based in Portugal, with regular collaboration at the Lisbon office.
Company
creates flexible, high-quality food experiences through data-driven systems that improve customer preferences and reduce food waste.
What you will do
- Build and improve recommendation and personalization models for meal and recipe discovery.
- Develop time-series forecasting models to improve demand prediction and logistics.
- Support retention and marketing initiatives through churn prediction and lifetime value modeling.
- Design and analyze A/B tests using robust statistical methods.
- Take models from exploration through validation, deployment, and production monitoring.
- Contribute to code reviews and knowledge sharing within the Data Tribe.
Requirements
- Around 3+ years of experience in applied data science.
- Strong foundations in machine learning, statistics, and time-series modeling.
- Strong proficiency in Python and SQL.
- Hands-on experience with scikit-learn, PyTorch, and XGBoost.
- Experience with modern data platforms like Snowflake, Looker, and Airflow.
- Ability to explain complex technical concepts to non-technical stakeholders.
Nice to have
- Experience in subscription, e-commerce, or consumer-product environments.
- Prior work in personalization, recommendations, or generative AI.
- Experience working in Agile, cross-functional product teams.
Culture & Benefits
- Hybrid work policy with regular in-person collaboration.
- 22 annual leave days plus tenure-based increases.
- 5 training days per year.
- Private health insurance provided by Tranquilidade.
- Food allowance via Coverflex.
- 24/7 confidential employee assistance program.
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