Junior Data Science Analyst Porto, Portugal

Metyis; Porto; Presencial; EUR 48 000 - 72 000; Tempo integral

Resumo da oferta

Metyis in Porto, Portugal, is seeking a data scientist to translate business problems into analytical solutions and deliver ML models end-to-end. You will work with Python, PySpark, and ML libraries, deploying models on cloud platforms and iterating with stakeholders.

The role emphasizes hands-on ML development, experimentation, and clear storytelling to drive data‑driven decisions within an international team. Fluency in English is essential.

Qualificações

• Master's degree in a quantitative field such as Data Science, Applied Mathematics, Computer Science, or Engineering.
• 0–3 years of experience in data science or advanced analytics.
• Proven ability to manage the full lifecycle of an ML solution; business understanding, data wrangling, and modelling, to production deployment and monitoring.
• Hands‑on experience with MLflow, Git, and MLOps frameworks.
• Solid programming skills in Python, including libraries like pandas, NumPy, scikit‑learn, matplotlib, seaborn, and PySpark.
• Experience with Azure (preferred) or AWS, especially in the context of Databricks, data pipelines, and model hosting.
• Good understanding of data warehousing and querying using SQL.
• Familiarity with statistical concepts (e.g., experiment design, hypothesis testing, p‑values).
• Experience or strong interest in Generative AI applications, such as LLM‑based solutions, prompt engineering, RAG pipelines, or AI‑assisted analytics is a plus.
• Exposure to Reinforcement Learning concepts is a plus, especially in areas like decision optimization, personalization, or next‑best‑action use cases.
• Strong communication and stakeholder management skills, including the ability to present technical insights to non‑technical audiences.
• Fluency in English (written and spoken).
• Experience within the retail and fashion industry (nice to have).
• Experience within international environments, consultancies, or a start‑up environment (nice to have).

Responsabilidades

• Translate business problems into analytical solutions, from requirement gathering to model deployment and monitoring.
• Build, deploy, and maintain machine learning models using modern MLOps frameworks.
• Leverage Databricks, Azure, or similar cloud infrastructure for scalable data processing and model deployment.
• Use Python, PySpark, and common ML libraries for data transformation, model development, and evaluation.
• Apply statistical methods to validate performance and derive actionable insights.
• Communicate findings and recommendations clearly through storytelling and visualizations.
• Collaborate with cross-functional stakeholders and contribute to customer‑centric strategies.
• Support team development through knowledge sharing and participation in agile working models.

Conhecimentos

ML basics

English fluency

Communication

Data science experience

Formação académica

Master's degree in Data Science or quantitative field

Ferramentas

MLflow

Git

MLOps

Azure

AWS

Databricks

SQL

Python

PySpark

Descrição da oferta de emprego

What we offer

Opportunity to accelerate the pace of digitalization through advanced technology, business intelligence, and analytics.

Driving high-impact insights enhancing decision making across the entire organization.

Interaction with senior business leaders on a regular basis to drive their business toward impactful change.

Become part of a fast-growing international and diverse team.

What you will do

Translate business problems into analytical solutions, from requirement gathering to model deployment and monitoring.

Build, deploy, and maintain machine learning models (e.g., Churn, CLV, Segmentation, Personalization) using modern MLOps frameworks (e.g., MLflow, Git).

Leverage Databricks, Azure, or similar cloud infrastructure for scalable data processing and model deployment.

Use Python, PySpark, and common ML libraries (e.g., scikit-learn, XGBoost, LightGBM) for data transformation, model development, and evaluation.

Apply statistical methods (e.g., hypothesis testing, A/B testing, confidence intervals) to validate performance and derive actionable insights.

Communicate findings and recommendations clearly through compelling storytelling and visualizations.

Collaborate with cross-functional stakeholders (e.g., Business teams, Data Engineering, IT teams) and contribute to customer‑centric strategies.

Support team development through knowledge sharing, collaboration, and participation in agile working models.

What you will bring

• 0–3 years of experience in data science or advanced analytics.
• A master's degree in a quantitative field such as Data Science, Applied Mathematics, Computer Science, or Engineering.
• Proven ability to manage the full lifecycle of an ML solution; business understanding, data wrangling, and modelling, to production deployment and monitoring.
• Hands‑on experience with MLflow, Git, and MLOps frameworks.
• Solid programming skills in Python, including libraries like pandas, NumPy, scikit‑learn, matplotlib, seaborn, and PySpark.
• Experience with Azure (preferred) or AWS, especially in the context of Databricks, data pipelines, and model hosting.
• Good understanding of data warehousing and querying using SQL.
• Familiarity with statistical concepts (e.g., experiment design, hypothesis testing, p‑values).
• Experience or strong interest in Generative AI applications, such as LLM‑based solutions, prompt engineering, RAG pipelines, or AI‑assisted analytics is a plus.
• Exposure to Reinforcement Learning concepts is a plus, especially in areas like decision optimization, personalization, or next‑best‑action use cases.
• Strong communication and stakeholder management skills, including the ability to present technical insights to non‑technical audiences.
• Fluency in English (written and spoken).
• Experience within the retail and fashion industry (nice to have).
• Experience within international environments, consultancies, or a start‑up environment (nice to have).