Data Science & Finance Intern — Hybrid

Novartis España; Barcelona; Hybrid; EUR 10,000 - 13,000; Full time

Benefits offered by this job

Hybrid working model

Paid internship

Job summary

Novartis is offering an Ubloom internship at its Barcelona Digital Finance Hub. The program targets students in quantitative fields, providing exposure to data science, ML and AI applied to finance processes.

You will work on forecasting, optimization, demand planning and causal analysis, supported by a multidisciplinary team of data scientists and software developers. Hybrid work and a 6-month to 12-month duration are planned.

Qualifications

• Student pursuing Bachelor's or Master's degree in Mathematics, Statistics, Computer Engineering, Physics or related quantitative field.
• Strong interest in Data Science, ML, AI and Advanced Analytics applied to real business problems.
• Python programming skills for data analysis and modeling tasks.

Responsibilities

• Support Data Science projects through full lifecycle: data extraction, cleaning, EDA, model development, validation and stakeholder communication.
• Contribute to design and development of ML solutions for forecasting and optimization use cases.
• Apply time series analysis and tree-based models to business problems.
• Develop Python code following best practices and testing frameworks.
• Validate model performance and results to ensure reliability and alignment with business objectives.
• Participate in daily project check-ins with project lead and weekly feedback meetings with internship tutor.

Skills

Python

Data Science

Machine Learning

Time Series

Forecasting

Education

Bachelor’s or Master’s student in quantitative field

Tools

Pandas

NumPy

Scikit-learn

TensorFlow

Job description

Novartis is offering an Ubloom internship at its Barcelona Digital Finance Hub. The program targets students in quantitative fields, providing exposure to data science, ML and AI applied to finance processes.

You will work on forecasting, optimization, demand planning and causal analysis, supported by a multidisciplinary team of data scientists and software developers. Hybrid work and a 6-month to 12-month duration are planned.