Lead Data Scientist, Workday AI Solutions

Kainos; United Kingdom; On-site; GBP 120,000 - 180,000; Full time

Job summary

Kainos in the United Kingdom is seeking a Lead Data Scientist for its Workday Products division. You will design and deliver advanced AI/ML solutions that power Workday applications, mentor a growing team, and drive innovation across the product suite.

You will collaborate with customers, lead initiatives in predictive analytics, anomaly detection, and intelligent automation, and ensure scalable, production-ready pipelines using modern ML tooling and cloud platforms.

Qualifications

• Typically 8+ years of industry experience in data science and AI leadership.
• Proven ability to design and deliver production-grade AI/ML solutions.
• Strong Python coding skills and experience with ML frameworks.
• Experience with MLOps, CI/CD, model versioning, monitoring.
• Ability to guide and mentor data science teams.
• Excellent stakeholder management and client engagement skills.

Responsibilities

• Lead the design and deployment of AI/ML solutions for Workday products.
• Mentor and grow a data science team; conduct performance reviews.
• Collaborate with customers to translate business needs into technical plans.
• Drive production pipelines, data engineering, and cloud deployments.
• Shape technical strategy and foster innovation.

Skills

Advanced statistics

Machine learning

Python

Distributed ML

Leadership & mentoring

Communication

Client engagements

Education

PhD in Computer Science / related field

MSc in Computer Science / Mathematics

Tools

TensorFlow

PyTorch

Scikit-learn

Docker

Kubernetes

Streamlit/Plotly

Job description

Kainos in the United Kingdom is seeking a Lead Data Scientist for its Workday Products division. You will design and deliver advanced AI/ML solutions that power Workday applications, mentor a growing team, and drive innovation across the product suite.

You will collaborate with customers, lead initiatives in predictive analytics, anomaly detection, and intelligent automation, and ensure scalable, production-ready pipelines using modern ML tooling and cloud platforms.