Data Scientist - Workday Products

🏢 Kainos · all 70 jobs
📍 United Kingdom
📅 Posted Aug 17, 2026 · via Himalayas
🏷 Associate Data Scientist, Data Scientist, Machine Learning Engineer, AI Engineer, Workday Products, Workday Data Specialist +6 more
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Join Kainos and Shape the Future

At Kainos , we’re problem solvers, innovators, and collaborators - driven by a shared mission to create real impact. Whether we’re transforming digital services for millions, delivering cutting-edge Workday solutions, or pushing the boundaries of technology, we do it together.

We believe in a people-first culture , where your ideas are valued, your growth is supported, and your contributions truly make a difference. Here, you’ll be part of a diverse, ambitious team that celebrates creativity and collaboration.

Ready to make your mark? Join us and be part of something bigger.

As an Associate Data Scientist within Kainos ' Workday Products division, you will support the delivery of AI and ML solutions across our fast-growing suite of Workday products, including Kainos Smart (Smart Test, Smart Audit and Smart Shield), Employee Document Management and Pay Transparency Analyzer, working closely with senior colleagues to learn and apply data science techniques on live client projects. This is a great opportunity to build your technical skills in a fast-paced environment, while contributing meaningfully to the team's delivery and growth.

Essential Experience:  

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Typically 2+ years of relevant industry experience.

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Foundational knowledge of mathematics, statistics, and machine learning principles, with an ability to apply these to derive insights from data under guidance.

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Working knowledge of Python programming, with an interest in writing clean, efficient code for AI/ML solutions.

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Hands-on experience, through study, internships, or personal projects, with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).

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Exposure to supporting the deployment of AI/ML models, working alongside engineering colleagues.

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Some exposure to generative AI concepts and tools (e.g., OpenAI GPT, Hugging Face Transformers), gained through coursework, internships, or personal projects.

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Basic awareness of cloud technologies (AWS, Azure, or GCP).

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Some experience creating visualisations or dashboards (e.g., Dash, Streamlit), through coursework or previous roles.

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Good interpersonal skills, with an interest in explaining technical concepts to non-technical audiences.

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Enthusiasm for learning from and collaborating with senior team members. 

Desirable Experience:  

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BSc in a quantitative field such as Computer Science, Machine Learning, Operational Research, or Statistics.

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Academic, internship, or personal projects delivering data science outputs.

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Basic familiarity with CI/CD pipelines or MLOps concepts.

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Exposure to containerisation technologies (e.g., Docker).

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Some experience working with relational or NoSQL databases (e.g., PostgreSQL, MongoDB).

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Familiarity with Workday data structures, APIs, or reporting tools is an advantage.

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Willingness to participate in team knowledge-sharing activities.

Embracing our differences  

At Kainos , we believe in the power of diversity, equity and inclusion. We are committed to building a team that is as diverse as the world we live in, where everyone is valued, respected, and given an equal chance to thrive.   We actively seek out talented people from all backgrounds, regardless of age, race, ethnicity, gender, sexual orientation, religion, disability, or any other characteristic that makes them who they are.   We also believe every candidate deserves a level playing field.

Our friendly talent acquisition team is here to support you every step of the way, so if you require any accommodations or adjustments, we encourage you to reach out.

We understand that everyone's journey is different, and by having a private conversation we can ensure that our recruitment process is tailored to your needs.

Originally posted on Himalayas

Flights + hotels

This role requires you to be in the United Kingdom. If that means relocating or flying in, it is worth checking fares before you commit to a start date.

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