Data Scientist

Location: Madrid, Community of Madrid, SpainPosition: Data Scientist (Machine Learning)Who We AreAt Kyndryl, we design, build, manage and modernize the mission‑critical technology systems that the world depends on every day. We are always moving forward – pushing ourselves to go further in our efforts to build a more equitable, inclusive world for our employees, customers and communities.The RoleWe’re looking for exceptional talent to join our AI Agentic Innovation Hub at Kyndryl The AI Agentic Innovation Hub stands as Kyndryl’s center of excellence for advanced and agentic artificial intelligence. Our mission is to lead the design and deployment of transformative AI solutions that bridge frontier research with real‑world impact — scalable, secure, and driven by measurable value.Job DescriptionAs a Data Scientist at Kyndryl’s AI Innovation Hub, you’ll be part of a team that turns data into intelligent, high‑impact solutions. You’ll collaborate with senior data scientists, ML engineers, and AI architects to design, train, and validate predictive and machine‑learning models that tackle real business and operational challenges. You’ll participate in every stage of the model lifecycle — from data exploration and feature engineering to modeling, evaluation, and documentation — helping transform raw data into actionable insights. This is a hands‑on, learning‑focused role in which you’ll work with modern technologies, contribute to scalable AI solutions, and grow your expertise within an environment that values experimentation, rigor, and curiosity.Your MissionCollaborate 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.Contribute to feature engineering, exploratory analysis, and performance optimization of models.Apply statistical and analytical techniques to extract meaningful patterns and insights from data.Assist in model deployment and monitoring within MLOps environments and cloud platforms.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.Essential Qualifications2–4 years of experience in data science, advanced analytics, or machine‑learning projects.Practical experience building and validating models for classification, regression, or segmentation tasks.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.Experience with model evaluation and validation (ROC, AUC, F1, RMSE, precision, recall, cross‑validation).Basic knowledge of cloud AI platforms (Azure ML, Vertex AI, SageMaker, Databricks).Awareness of model versioning and experiment tracking tools (MLflow, DVC).Understanding of Responsible AI concepts — bias mitigation, transparency, and interpretability.Education & CertificationsBachelor’s or Master’s degree in Computer Engineering, Mathematics, Statistics, Physics, Data Science, or related field.Postgraduate studies (Master’s or PhD) in Artificial Intelligence, Statistics, or Computational Science highly valued.Complementary training in Applied Data Science or Machine Learning Engineering is a plus.Continuous learning mindset and commitment to staying current with advances in ML, MLOps, and applied AI.Preferred SkillsExperience deploying models on cloud‑based ML platforms (Azure ML, Vertex AI, SageMaker, Databricks, OpenShift AI).Exposure to deep learning, classical NLP, or time‑series modeling.Familiarity with automated retraining, model versioning, and drift detection frameworks.Practical knowledge of data visualization and business intelligence tools for model interpretation.Experience leading or mentoring junior team members in applied data‑science projects.Ability to work in agile, cross‑functional teams with shared ownership and accountability.Strong focus on explainability, traceability, and responsible AI design.Soft SkillsAnalytical and impact‑driven mindset, able to define hypotheses and validate them through experimentation.Clear and concise communication, transforming technical findings into actionable insights for business teams.Business‑oriented problem‑solving, understanding functional needs and converting them into data‑driven solutions.Collaborative leadership, fostering teamwork, mentoring, and continuous knowledge sharing.Attention to quality and reproducibility, ensuring every model is robust, auditable, and maintainable.Curiosity and lifelong learning attitude, keeping pace with new algorithms, frameworks, and methodologies.Adaptability, thriving in dynamic environments that blend research, engineering, and innovation.Being YouDiversity is a whole lot more than what we look like or where we come from, it’s how we think and who we are. We welcome people of all cultures, backgrounds, and experiences. Our Kyndryl Inclusion Networks are one of many ways we create a workplace where all Kyndryl employees can find and provide support and advice. Bring your whole self to work and support the activation of our equitable culture.What You Can ExpectWith state‑of‑the‑art resources and Fortune 100 clients, every day is an opportunity to innovate, build new capabilities, relationships, and value. Kyndryl cares about your well‑being and offers benefits that reflect the diversity of our employees and support you and your family through the moments that matter. Our employee learning programs give access to certifications from Microsoft, Google, Amazon, Skillsoft, and many more. Through our company‑wide volunteering platform, you can donate, start fundraisers, volunteer, and search over 2 million non‑profit organizations. We invest heavily in you to succeed together.Get ReferredIf you know someone who works at Kyndryl, submit their email address when asked “How Did You Hear About Us” during the application process and select “Employee Referral.”#J-18808-Ljbffr