Data Scientist

Who We Are

At Kyndryl, we run and reimagine the mission-critical technology systems that drive advantage for the world's leading businesses. We are at the heart of progress; with proven expertise and a continuous flow of AI-powered insight, enabling smarter decisions, faster innovation, and a lasting competitive edge. For our people-Kyndryls-that means doing purposeful work that powers human progress. Join us and experience a flexible, supportive environment where your well-being is prioritized and your potential can thrive.

The Role

We'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.
Built upon a team of exceptional talent and cutting-edge technology, the Hub embodies a spirit of bold innovation and disciplined execution - an elite unit within one of the world's leading technology companies. With national reach and global ambition, we partner with major organizations to tackle their most complex challenges, pioneering the next generation of intelligent, autonomous, and trusted systems that redefine what AI can achieve.

• 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.
• 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.

• 2-4 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.

• Bachelor's degree in Computer Engineering, Mathematics, Statistics, Physics, Data Science, or related field.
• Postgraduate or Master's degree in Artificial Intelligence, Machine Learning, or Data Analytics is valued.
• Complementary training or certifications in Machine Learning, Data Science, or MLOps are a plus.
• Demonstrated interest in continuous learning and professional growth in applied AI.

• Exposure to data pipelines, ETL processes, and data preparation workflows.
• Familiarity with SQL/NoSQL databases and working with APIs for data access.
• Experience supporting model integration and testing within production environments.
• Curiosity for new algorithms, frameworks, and emerging tools in AI and MLOps.
• Experience with data visualization and presentation of insights to cross-functional teams.
• Ability to collaborate effectively in agile, fast-paced environments.

• Analytical curiosity and a scientific mindset, eager to understand problems deeply and find evidence-based solutions.
• Clear and structured communication, both in technical documentation and team collaboration.
• Logical and organized thinking, applying rigorous methodologies to problem-solving.
• Collaborative spirit, working proactively with technical and business profiles to deliver results.
• Attention to detail and quality, ensuring reproducibility and reliability in models and analyses.
• Continuous learning attitude, exploring new techniques, algorithms, and tools to strengthen the Hub's capabilities.
• Adaptability and growth orientation, thriving in a dynamic, innovation-driven environment.