Data Scientist (Machine Learning)

As 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. If you're passionate about data, eager to learn from experienced professionals, and ready to contribute to cutting-edge AI initiatives for leading global clients, this is your opportunity to build the foundation of your career in applied data science.

Your Mission

• 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. Who You Are

• 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. Education & Certifications

• Bachelor'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 are 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 Skills

• Experience 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 Skills

• Analytical 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. #AgenticAI -paced environments.

Diversity 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. But we're not doing it single-handily: Our Kyndryl Inclusion Networks are only one of many ways we create a workplace where all Kyndryls can find and provide support and advice. This dedication to welcoming everyone into our company means that Kyndryl gives you - and everyone next to you - the ability to bring your whole self to work, individually and collectively, and support the activation of our equitable culture. That's the Kyndryl Way. What You Can Expect With state-of-the-art resources and Fortune 100 clients, every day is an opportunity to innovate, build new capabilities, new relationships, new processes, and new value. Kyndryl cares about your well-being and prides itself on offering benefits that give you choice, reflect the diversity of our employees and support you and your family through the moments that matter - wherever you are in your life journey. Our employee learning programs give you access to the best learning in the industry to receive certifications, including Microsoft, Google, Amazon, Skillsoft, and many more. Through our company-wide volunteering and giving platform, you can donate, start fundraisers, volunteer, and search over 2 million non-profit organizations. At Kyndryl, we invest heavily in you, we want you to succeed so that together, we will all succeed.