Lead Data Scientist

As a Lead Data Scientist within the Workday Practice at Kainos, you will play a pivotal role in shaping the AI and analytics strategy for our Workday product offerings. You will lead the design and delivery of advanced AI/ML solutions that improve the functionality, scalability, and efficiency of Workday systems. You will focus on cuttingedge 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.

Essential 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.

• 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 Tableau, Plotly, Dash, or D3.js.

• Proven ability to lead and mentor data science teams, fostering collaboration and professional growth.

• 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.

• 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.

• Expertise in deep learning techniques (e.g., CNNs, RNNs, Transformers) and their application in NLP, time-series forecasting, or recommendation systems.

• 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.

Who you are:

• Determined – you’re flexible and overcome obstacles to get the job done to achieve personal and team goals. 

• Creative – you actively look for better ways to do things using the latest AI technologies to find fresh solutions to complex problems 

• Honest – always constructive when giving or receiving feedback, being transparent and truthful when dealing with others 

• Respectful – you treat others as you would like to be treated being encouraging, accepting and supportive to everyone you deal with  

• Cooperative – you share information, knowledge and experience, understanding the mutual benefits of team working