Hybrid Senior Data Scientist – Machine Learning

at Kyndryl

• Job title: Senior Data Scientist – Machine Learning
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, ETL, NoSQL, Pandas, Python, PyTorch, Scikit-Learn, Tensorflow
• Location requirements: Hybrid, Madrid, Spain

About the role

• Senior Data Scientist developing advanced machine learning models to solve complex business problems at Kyndryl's AI Innovation Hub in Spain. Leading model lifecycle with collaboration among AI architects and engineers.

Responsibilities

• Design, develop, and validate machine learning models that solve complex business problems through data
• Translate functional and strategic requirements into predictive, prescriptive, and analytical solutions
• Lead the end-to-end model lifecycle — from data exploration and feature engineering to deployment and monitoring
• Collaborate with architects and engineers to ensure seamless integration of models into production environments
• Apply best practices in MLOps, ensuring models are traceable, reproducible, and governed throughout their lifecycle
• Evaluate and optimize model performance using robust metrics and explainability techniques
• Document, communicate, and present insights clearly to both technical and business audiences
• Mentor junior data scientists, promoting technical excellence, innovation, and a culture of learning within the Hub

Requirements

• 4+ years of experience developing predictive models and advanced analytics solutions in business contexts
• Proven expertise across the entire model lifecycle — data preparation, modeling, validation, evaluation, and deployment
• Strong programming skills in Python and core ML libraries (Pandas, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, StatsModels)
• Solid foundation in statistical analysis, supervised and unsupervised learning, regression, classification, clustering, anomaly detection, and forecasting
• Experience with feature engineering, data transformation, and data augmentation for model improvement
• Knowledge of MLOps and CI/CD for models, using tools such as MLflow, DVC, Airflow, Kubeflow, or Vertex Pipelines
• Skilled in model evaluation and optimization (ROC, AUC, F1, RMSE, SHAP, explainability techniques)
• Familiarity with DataOps principles, APIs, ETL pipelines, and relational or NoSQL databases
• Understanding of AI governance, privacy, and compliance frameworks (GDPR, Responsible AI)

Benefits

• Professional development opportunities
• Employee learning programs that give access to the best learning in the industry
• Company-wide volunteering and giving platform