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Data Scientist at Discovery Limited
Discovery Limited
August 31, 2026
Full-time
On-site
Key Purpose
The purpose of this managerial role is to lead the delivery of Agentic AI initiatives within the Data Science Lab, with accountability for translating high-value operational and clinical opportunities into safe, scalable, measurable AI-enabled products. The role will focus primarily on agentic workflows that combine large language models, structured data, retrieval, business rules, human-in-the-loop controls and integration with Discovery systems.
The successful candidate will manage cross-functional delivery across data science, engineering, systems, operations, clinical, actuarial, privacy, security and business stakeholders. They will be expected to shape the roadmap, prioritise use cases, guide technical and product decisions, manage risks, and ensure that Agentic AI solutions move from prototype through evaluation, governance approval, production deployment and measurable adoption.
The role will lead work that:
Delivers Agentic AI products that improve operational efficiency, servicing quality, clinical decision support and member outcomes.
Turns ambiguous business problems into prioritised AI use cases with clear value cases, success measures, risks and delivery plans.
Builds and evaluates AI-enabled workflows using large language models, retrieval, structured data, business rules and human oversight.
Maintains a disciplined culture of measurement, governance, experimentation, adoption and continuous improvement.
Areas of responsibility may include but are not limited to:
Agentic AI Product Delivery and Roadmap Support
Support the development and delivery of the Agentic AI roadmap for priority DS Lab initiatives, helping move opportunities from concept through implementation and adoption.
Contribute to use-case prioritisation by balancing business value, feasibility, risk, operational readiness and strategic alignment.
Coordinate with multidisciplinary teams to shape AI-enabled workflows that may combine LLMs, retrieval, structured data, business rules, workflow orchestration and human-in-the-loop controls.
Help define success measures, evaluation approaches, rollout considerations and feedback loops so that solutions remain practical, measurable and aligned to business needs.
Technical Enablement, Governance and Responsible AI
Work with technical specialists to guide appropriate choices around AI architecture, model selection, prompt and tool design, retrieval approaches, evaluation methods, deployment patterns and production monitoring.
Ensure Agentic AI solutions are developed with appropriate attention to privacy, security, data governance, responsible AI and operational risk requirements.
Create clear documentation of assumptions, controls, limitations, failure modes, escalation paths and audit requirements.
Promote alignment with Group AI standards, privacy requirements and security approval processes.
Stakeholder Management and Business Adoption
Work with business, operational, clinical, actuarial, systems and governance stakeholders to build alignment on priorities, decisions, risks and delivery dependencies.
Translate complex AI capabilities into practical business language, implementation considerations, operating model implications and expected benefits.
Drive adoption by helping ensure AI products are embedded into real workflows, with appropriate training, feedback mechanisms, change management and continuous improvement.
Data Science, Experimentation and Personalisation
Contribute to adjacent data science work where it supports Agentic AI delivery, including predictive modelling, causal inference, experimentation, personalisation, optimisation and impact measurement.
Encourage analytical approaches that are appropriate, reproducible, well-documented and aligned with the business decision or workflow being improved.
Use measurement and experimentation thinking to help assess whether solutions are working, for whom, under what conditions, and with what operational or member impact.
Technical Skills
Required
Strong experience delivering data science, machine learning, generative AI or Agentic AI solutions in a business environment.
Practical understanding of LLMs, retrieval-augmented generation, AI agents, workflow orchestration, evaluation, guardrails and human-in-the-loop design.
Proficiency in Python and SQL, with the ability to guide technical teams on analytical design, code quality, reproducibility and production readiness.
Strong foundations in statistics, machine learning, experimental design, model evaluation and impact measurement.
Ability to manage delivery across technical, operational, governance and stakeholder workstreams.
Advantageous
Experience with cloud platforms, preferably GCP, and production AI or data science deployment patterns.
Experience in healthcare, insurance, clinical operations, servicing operations, behavioural science or personalisation contexts.
Experience with privacy, security, model risk, responsible AI or technology governance processes.
Education and Experience
Honours, Master's or PhD degree in quantitative disciplines such as Computer Science, Data Science, Statistics, Mathematics, Actuarial Science, Operations Research, Industrial Engineering or Applied Mathematics.
Demonstrable experience leading or managing data science, machine learning, AI, analytics or technology delivery initiatives.
Experience managing stakeholders, delivery plans, technical decisions, risks and measurable outcomes across multidisciplinary teams.
Equivalent qualifications or alternative pathways will be considered where supported by strong technical, analytical and managerial capability.
Personal Attributes and Skills
Commercially minded and motivated to use Agentic AI to solve meaningful healthcare, operational and business challenges.
Able to lead through ambiguity, structure complex problems and make pragmatic decisions under uncertainty.
Strong stakeholder management and executive communication skills, with the ability to explain AI risks, trade-offs and value clearly.
Evidence-based, delivery-focused and comfortable balancing speed, quality, safety and governance.
Collaborative, accountable and able to build trust across technical, operational and governance teams.
Curious, well-read and committed to keeping the team close to the cutting edge of applied AI while remaining grounded in business value.
Aligned with Discovery's values and core purpose.
The purpose of this managerial role is to lead the delivery of Agentic AI initiatives within the Data Science Lab, with accountability for translating high-value operational and clinical opportunities into safe, scalable, measurable AI-enabled products. The role will focus primarily on agentic workflows that combine large language models, structured data, retrieval, business rules, human-in-the-loop controls and integration with Discovery systems.
The successful candidate will manage cross-functional delivery across data science, engineering, systems, operations, clinical, actuarial, privacy, security and business stakeholders. They will be expected to shape the roadmap, prioritise use cases, guide technical and product decisions, manage risks, and ensure that Agentic AI solutions move from prototype through evaluation, governance approval, production deployment and measurable adoption.
The role will lead work that:
Delivers Agentic AI products that improve operational efficiency, servicing quality, clinical decision support and member outcomes.
Turns ambiguous business problems into prioritised AI use cases with clear value cases, success measures, risks and delivery plans.
Builds and evaluates AI-enabled workflows using large language models, retrieval, structured data, business rules and human oversight.
Maintains a disciplined culture of measurement, governance, experimentation, adoption and continuous improvement.
Areas of responsibility may include but are not limited to:
Agentic AI Product Delivery and Roadmap Support
Support the development and delivery of the Agentic AI roadmap for priority DS Lab initiatives, helping move opportunities from concept through implementation and adoption.
Contribute to use-case prioritisation by balancing business value, feasibility, risk, operational readiness and strategic alignment.
Coordinate with multidisciplinary teams to shape AI-enabled workflows that may combine LLMs, retrieval, structured data, business rules, workflow orchestration and human-in-the-loop controls.
Help define success measures, evaluation approaches, rollout considerations and feedback loops so that solutions remain practical, measurable and aligned to business needs.
Technical Enablement, Governance and Responsible AI
Work with technical specialists to guide appropriate choices around AI architecture, model selection, prompt and tool design, retrieval approaches, evaluation methods, deployment patterns and production monitoring.
Ensure Agentic AI solutions are developed with appropriate attention to privacy, security, data governance, responsible AI and operational risk requirements.
Create clear documentation of assumptions, controls, limitations, failure modes, escalation paths and audit requirements.
Promote alignment with Group AI standards, privacy requirements and security approval processes.
Stakeholder Management and Business Adoption
Work with business, operational, clinical, actuarial, systems and governance stakeholders to build alignment on priorities, decisions, risks and delivery dependencies.
Translate complex AI capabilities into practical business language, implementation considerations, operating model implications and expected benefits.
Drive adoption by helping ensure AI products are embedded into real workflows, with appropriate training, feedback mechanisms, change management and continuous improvement.
Data Science, Experimentation and Personalisation
Contribute to adjacent data science work where it supports Agentic AI delivery, including predictive modelling, causal inference, experimentation, personalisation, optimisation and impact measurement.
Encourage analytical approaches that are appropriate, reproducible, well-documented and aligned with the business decision or workflow being improved.
Use measurement and experimentation thinking to help assess whether solutions are working, for whom, under what conditions, and with what operational or member impact.
Technical Skills
Required
Strong experience delivering data science, machine learning, generative AI or Agentic AI solutions in a business environment.
Practical understanding of LLMs, retrieval-augmented generation, AI agents, workflow orchestration, evaluation, guardrails and human-in-the-loop design.
Proficiency in Python and SQL, with the ability to guide technical teams on analytical design, code quality, reproducibility and production readiness.
Strong foundations in statistics, machine learning, experimental design, model evaluation and impact measurement.
Ability to manage delivery across technical, operational, governance and stakeholder workstreams.
Advantageous
Experience with cloud platforms, preferably GCP, and production AI or data science deployment patterns.
Experience in healthcare, insurance, clinical operations, servicing operations, behavioural science or personalisation contexts.
Experience with privacy, security, model risk, responsible AI or technology governance processes.
Education and Experience
Honours, Master's or PhD degree in quantitative disciplines such as Computer Science, Data Science, Statistics, Mathematics, Actuarial Science, Operations Research, Industrial Engineering or Applied Mathematics.
Demonstrable experience leading or managing data science, machine learning, AI, analytics or technology delivery initiatives.
Experience managing stakeholders, delivery plans, technical decisions, risks and measurable outcomes across multidisciplinary teams.
Equivalent qualifications or alternative pathways will be considered where supported by strong technical, analytical and managerial capability.
Personal Attributes and Skills
Commercially minded and motivated to use Agentic AI to solve meaningful healthcare, operational and business challenges.
Able to lead through ambiguity, structure complex problems and make pragmatic decisions under uncertainty.
Strong stakeholder management and executive communication skills, with the ability to explain AI risks, trade-offs and value clearly.
Evidence-based, delivery-focused and comfortable balancing speed, quality, safety and governance.
Collaborative, accountable and able to build trust across technical, operational and governance teams.
Curious, well-read and committed to keeping the team close to the cutting edge of applied AI while remaining grounded in business value.
Aligned with Discovery's values and core purpose.