Data Scientist

Matrix Eastern Europe; Madrid; Vor Ort; EUR 52.000 - 78.000; Vollzeit

Zusammenfassung

Matrix Eastern Europe is seeking a highly motivated Data Scientist to join our growing team. You will work on large-scale datasets, develop advanced analytical models, and collaborate with product, engineering, and business stakeholders to drive measurable impact.

This role blends advanced analytics, machine learning, and business problem-solving to deliver scalable solutions for customer implementations and production deployments.

Qualifikationen

• Strong analytical and problem-solving abilities with data-driven decision making.
• Solid foundation in statistics and modeling techniques for ML projects.
• Excellent communication skills to convey complex concepts to varied audiences.

Aufgaben

• Design and implement data science solutions throughout the project lifecycle.
• Analyze large datasets to identify trends, anomalies, and insights.
• Develop and optimize machine learning models and analytics workflows.
• Perform feature engineering and data preparation for predictive modeling initiatives.
• Collaborate with Product, Engineering, and Customer Success to translate business needs into solutions.
• Lead data science aspects of customer implementations and deployments.
• Build strong technical relationships with customers and external partners.
• Provide recommendations to improve products and analytics capabilities.
• Deliver training and knowledge transfer to end users and stakeholders.
• Support investigation and resolution of data-related challenges.

Kenntnisse

Analytical thinking

Statistical modeling

Communication

Ausbildung

Bachelor’s degree in Data Science/CS/Math/Statistics/Economics

Tools

Python

PySpark

MLflow

Airflow

Jobbeschreibung

We are seeking a highly motivated Data Scientist to join our growing team and help deliver innovative machine learning and analytics solutions for complex business challenges. In this customer-facing role, you will work with large-scale datasets, develop advanced analytical models, and collaborate cross-functionally with product, engineering, and business stakeholders to drive measurable impact.

This role is ideal for a data scientist who enjoys combining advanced analytics, machine learning, and business problem-solving to create impactful, scalable solutions.

Responsibilities

• Design and implement data science solutions throughout the project lifecycle.
• Analyze large and complex datasets to identify trends, anomalies, and actionable insights.
• Develop and optimize machine learning models and advanced analytics workflows.
• Perform feature engineering and data preparation for predictive modeling initiatives.
• Collaborate with Product, Engineering, and Customer Success teams to translate business requirements into technical solutions.
• Lead the data science aspects of customer implementations, pilots, and production deployments.
• Build strong technical relationships with customers and external partners.
• Provide recommendations to improve products, algorithms, and analytical capabilities.
• Deliver training and knowledge transfer to end users and stakeholders.
• Support investigation and resolution of data-related and analytical challenges.

Requirements

• Bachelor’s degree in Data Science, Computer Science, Mathematics, Statistics, Economics, or a related quantitative field.
• 2+ years of experience in Data Science, Data Engineering, Machine Learning, or Big Data development.
• Strong programming skills in Python and experience with PySpark.
• Hands-on experience working with large-scale data processing frameworks and machine learning pipelines.
• Experience with ML lifecycle and workflow management tools such as MLflow, Airflow, or similar platforms.
• Strong analytical, problem-solving, and statistical modeling skills.
• Ability to communicate complex technical concepts to both technical and non-technical audiences.
• Experience working directly with customers or external stakeholders is a plus.

Advantages

• Experience in financial services, fraud detection, risk analytics, compliance, or transaction monitoring.
• Familiarity with cloud-based data and machine learning environments.
• Experience investigating anomalies and extracting insights from high-volume datasets.
• Knowledge of machine learning model deployment and monitoring best practices.