As a Lead Data Scientist within Kainos’ Workday Products division, you will play a pivotal role in actively contributing to the AI solutions behind our fast growing suite of Workday products– including Kainos Smart(Smart Test, Smart Audit and Smart Shield), Employee Document Management and Pay Transparency Analyzer You will lead the design and delivery of advanced AI/ML solutions that improve the functionality, scalability, and efficiency of our Workday product suiteYou will focus on cutting edge innovations, such as predictive analytics for workforce planning, anomaly detection in financial processes, and intelligent automation for Workday applications. You will collaborate closely with customers, mentor your team, and provide thought leadership across the organization. Alongside this strategic and technical ownership, you will carryline management responsibilities, including the development, appraisal, and career progression of members of your team.

Essential Experience:

• Typically 8+ years of relevant industry experience, or a relevant PhD combined with 4-5 years of industry experience.

• Significant experience applying advanced statistical techniques, machine learning, and AI principles to solve complex business problems.

• Strong programming skills in Python, with an emphasis on writing clean, efficient, and maintainable code to enable scalable and production-grade AI/ML solutions.

• Extensive experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch), with the ability to design, implement, and optimise scalable solutions while guiding teams in the effective use of these tools.

• Extensive expertise in designing, deploying, and maintaining production-grade AI/ML solutions, including pipelines, MLOps practices (e.g., CI/CD pipelines, model versioning, monitoring), and seamless integration with enterprise systems such as Workday.

• Extensive experience designing and implementing generative AI use cases, leveraging large language models (e.g., OpenAI GPT, Hugging Face Transformers) to deliver scalable solutions for tasks such as conversational AI, document summarisation, or content creation.

• Proven experience with containerisation and orchestration technologies (e.g., Docker, Kubernetes), including their use in designing scalable, cloud-native AI/ML systems.

• Proficiency in data engineering, including data wrangling, cleansing, and creating pipelines that integrate seamlessly into production environments.

• Extensive experience in cloud environments (AWS, Azure, or GCP), including leveraging cloud-native AI tools like SageMaker, Vertex AI, or Azure ML Studio.

• Demonstrable expertise in creating interactive dashboards and visual analytics using tools such as Streamlit, Plotly, Dash, or D3.js.

• Proven experience leading, mentoring, and formally line-managing data science teams, including conducting performance appraisals and supporting career development and progression.

• Strong interpersonal and communication skills, with a track record of managing client engagements and translating business requirements into actionable technical solutions.

Desirable Experience:

• Advanced degree (MSc or PhD) in Computer Science, Machine Learning, Operational Research, Statistics, or a related field.

• Proven track record of delivering AI solutions in enterprise SaaS environments, particularly for Workday systems.

• Advanced proficiency in relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, DynamoDB).

• Familiarity with Workday APIs, Workday Prism Analytics, and automated testing frameworks like Kainos Smart.

• Knowledge of data engineering and analytics platforms such as Databricks, with experience in leveraging them for scalable data processing and machine learning workflows.

• Active participation in knowledgesharing activities, such as conferences, blogs, or internal workshops, to promote thought leadership.