Senior Data Scientist - Workday Products

As a Senior Data Scientist within Kainos’ Workday Products division, you'll be responsible for developing high quality AI and ML solutions for 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 work on the design and delivery of advanced AI/ML solutions that improve the functionality, scalability and efficiency of our Workday product suite. You may also carry some formal line management responsibilities, including appraisals, for more junior members of the team. 

Essential Experience:  

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

• Proficient in applying mathematics, statistics, and machine learning principles to derive actionable insights from complex datasets. 

• Proficient in Python programming, with a focus on writing clean, efficient, and maintainable code for developing and deploying reliable AI/ML solutions in production environments. 

• Hands-on experience using machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) to design and implement solutions. 

• Experience deploying AI/ML models to production systems in collaboration with engineering teams. 

• Experience working with generative AI use cases, leveraging large language models (e.g., OpenAI GPT, Hugging Face Transformers) to solve real-world problems such as text summarisation, chatbots, or content generation. 

• Basic experience with cloud technologies (e.g., AWS, Azure, or GCP). 

• Experience creating interactive visualizations and dashboards using tools such as Dash or Streamlit to communicate findings effectively. 

• Strong interpersonal skills, with the ability to lead client projects and explain technical concepts in non-technical terms. 

Desirable Experience:  

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

• Proven track record of delivering data science projects, especially in enterprise software or SaaS environments. 

• Basic familiarity with CI/CD pipelines and MLOps practices, including automated testing, model versioning, and monitoring workflows. 

• Hands-on experience with containerisation and orchestration technologies (e.g., Docker, Kubernetes) to support AI/ML model deployment. 

• Proficiency in cleansing, filtering, and integrating data from diverse sources, including relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB, DynamoDB). 

• Familiarity with Workday data structures, APIs, and reporting tools. 

• Demonstrable experience mentoring junior team members, with some involvement in formal performance appraisal processes, and fostering collaboration within teams.  

• Prior involvement in knowledge-sharing activities within teams or through public forums (conferences, blogs, etc.).