Lead Data Scientist - Workday Products
Role details
Job location
Tech stack
Requirements
-
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 knowledge sharing activities, such as conferences, blogs, or internal workshops, to promote thought leadership.