Senior Data Scientist: ML, Data Pipelines & Impact

ES14 (FCRS = ES014) Sandoz Farmacéutica, S.A.; Madrid; On-site; EUR 65,000 - 90,000; Full time

Job summary

Sandoz is seeking an experienced data scientist/ML engineer to tackle complex business problems by building scalable ML solutions. You will collaborate across teams to translate insights into actionable business impact and contribute to production-grade data pipelines.

The role emphasizes advanced analytics, forecasting, and the deployment of models in production, with a focus on delivering value across global healthcare initiatives.

Qualifications

• 4-6 years in data science or analytics roles, delivering models in production
• Fluent English is a must
• Proven experience with ML models (classification, regression, clustering) and forecasting
• Strong programming (Python) and SQL experience
• Exposure to big data tools and cloud platforms
• Experience building data pipelines and production-ready code

Responsibilities

• Drives model development of complex solutions using ML/deep learning and advanced statistical methods
• Selects appropriate techniques for the question/problem
• Develops/improves data management techniques, algorithms, methods and quantitative approaches
• Extracts insight from structured and unstructured data
• Applies external research methodologies to business problems and proposes scalable approaches
• Produces actionable insights and communicates recommendations to stakeholders
• Reporting of technical complaints/adverse events within 24 hours (where applicable)
• Distribution of marketing samples (where applicable)

Skills

Python

SQL

Big data

Machine learning

Data visualization

Communication

Problem solving

Forecasting

Feature engineering

Tools

Airflow

MLflow

Spark

Databricks

Hadoop

Job description

Sandoz is seeking an experienced data scientist/ML engineer to tackle complex business problems by building scalable ML solutions. You will collaborate across teams to translate insights into actionable business impact and contribute to production-grade data pipelines.

The role emphasizes advanced analytics, forecasting, and the deployment of models in production, with a focus on delivering value across global healthcare initiatives.