Data Scientist

1w1 week ago

Kyndryl

ES · Full-time · €40,000 – €60,000

About this role

At Kyndryl, we run and reimagine the mission-critical technology systems that drive advantage for the world’s leading businesses. The AI Agentic Innovation Hub is Kyndryl’s center of excellence for advanced and agentic artificial intelligence. We lead the design and deployment of transformative AI solutions bridging frontier research with real-world impact—scalable, secure, and driven by measurable value.

As a Data Scientist, you’ll collaborate with senior data scientists, ML engineers, and AI architects to design, train, and validate predictive and machine learning models tackling real business challenges. Participate in every stage of the model lifecycle—from data exploration and feature engineering to modeling, evaluation, and documentation. Transform raw data into actionable insights using modern technologies.

This hands-on, learning-focused role is within an elite unit embodying bold innovation and disciplined execution. Work with exceptional talent and cutting-edge technology in a collaborative environment. Partner with major organizations to pioneer intelligent, autonomous, and trusted systems.

Grow your expertise in an environment valuing experimentation, rigor, and curiosity. Stay up to date with new algorithms, frameworks, and best practices in data science and applied AI. Contribute to a knowledge-sharing culture while doing purposeful work that powers human progress.

Requirements

Approximately 2–4 years of experience in data science, advanced analytics, or machine learning projects
Solid skills in Python and core data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib, XGBoost, LightGBM)
Familiarity with neural networks and deep learning frameworks (TensorFlow, PyTorch)
Strong understanding of data preprocessing, handling missing values, unbalanced datasets, and outlier detection
Understanding of Responsible AI concepts—bias mitigation, transparency, and interpretability
Genuine curiosity and motivation to learn, experiment, and grow in applied AI

Responsibilities

Collaborate with senior scientists and engineers to develop and validate machine learning and predictive models
Participate in the end-to-end model lifecycle—from data collection and preparation to training, evaluation, and documentation
Apply statistical and analytical techniques to extract meaningful patterns and insights from data
Document experiments and ensure transparency, reproducibility, and traceability of results
Stay up to date with new algorithms, frameworks, and best practices in data science and applied AI
Actively contribute to a collaborative, knowledge-sharing culture within the Hub