Senior Lead, Data Science

About the role

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. As a Data Scientist at Kyndryl you are the bridge between business problems and innovative solutions, using a powerful blend of well-defined methodologies, statistics, mathematics, domain expertise, consulting, and software engineering. You'll wear many hats, and each day will present a new puzzle to solve, a new challenge to conquer. As a Full-Stack Senior Lead Data Scientist , you will bring a diverse skill set across data collection, analysis, model development, and deployment. You will collaborate closely with the data engineer and data scientist to develop predictive models, implement machine learning algorithms, and deploy end-to-end solutions that optimize our workforce strategies. This role demands proficiency across the full data science lifecycle, from data wrangling to delivering insights via advanced visualizations. Specifically, you'll be responsible for: End-to-End Data Science Solutions : Build, deploy, and maintain full-stack data science solutions, from data extraction to machine learning model deployment and monitoring. Collaboration with Data Engineering and Science : Partner with the data engineer and data scientist to ensure clean, structured data is available for analysis and predictive modeling. Participate in designing scalable data pipelines and architectures to support analytical and machine learning workflows. Machine Learning Model Development : Design and implement advanced machine learning models, including regression, classification, and clustering techniques, to predict workforce needs and identify skills gaps. API Development and Integration : Develop APIs to integrate machine learning models into enterprise applications and workflows, enabling real-time decision-making. Data Management : Ensure data integrity, consistency, and accuracy across multiple platforms, working closely with data governance teams to align processes. Advanced Data Visualization : Use data visualization tools (e.g., Tableau, Power BI) to translate complex analytical insights into actionable business recommendations for HR, business leaders, and other stakeholders. Optimization Models : Implement optimization algorithms to recommend actions such as hiring, training, or workforce engagement based on model outputs and business constraints. Performance Monitoring and Improvement : Continuously track model performance and refine processes for model efficiency and scalability. Your Future at Kyndryl Every position at Kyndryl offers a way forward to grow your career. We have opportunities that you won't find anywhere else, including hands-on experience, learning opportunities, and the chance to certify in all four major platforms. Whether you want to broaden your knowledge base or narrow your scope and specialize in a specific sector, you can find your opportunity here.

Requirements

• Required Skills and Experience
• Master's in Data Science, Computer Science, Statistics, or a related field.
• 5-8 years of experience as a data scientist with a focus on full-stack data science, including model building, deployment, and API development.
• Proficiency in Python, R, or similar languages for statistical modeling and machine learning.
• Experience with big data platforms (e.g., Hadoop, Spark) and cloud services (AWS, Azure, GCP).
• Strong SQL skills and familiarity with both relational and NoSQL databases.
• Expertise in building and deploying machine learning models (e.g., TensorFlow, PyTorch).
• Experience with data visualization tools such as Power BI, Tableau, or similar.
• Familiarity with RESTful APIs and integrating machine learning models into business applications.
• Strong understanding of optimization algorithms, data architecture, and performance monitoring.
• Excellent communication skills to translate technical findings into actionable business insights.
• Proficient in English language - verbal and written
• Preferred Skills and Experience
• Familiarity with workforce analytics, HR data, and related systems.
• Knowledge of graph databases (e.g., Neo4j, GraphQL) is a plus.
• Being You
• The "Kyn" in Kyndryl means kinship, whi

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