Job Summary

In this role, you will act as a bridge between business stakeholders and technical data teams—but with a strong emphasis on hands-on data science, statistical modelling, and machine learning development rather than purely coordination. You will design, build, and deploy scalable models that generate actionable insights and power intelligent sales processes. As part of the Reporting team within WW Sales Enablement and Transformation, you will lead the development of advanced analytics solutions, applying machine learning, experimentation, and statistical techniques to improve sales performance, forecasting accuracy, and operational efficiency. This role requires a combination of deep technical expertise, strong business intuition, and end-to-end ownership of data science solutions, from problem framing through production deployment and impact measurement. Description In this role, you will: Design and build production-grade machine learning systems across the sales data platform, including building MCP servers, recommendation systems, agentic summarisation frameworks, and RAG pipelines at scale. • Deploy models into batch and real-time environments, ensuring scalability, reliability and performance • Engineer solutions using LLMs and foundation models to build applications such as chatbots, semantic search engines, and summary generation tools for a data reporting platform. • Design modular APIs, SDKs, and micro-services to integrate LLMs, RAG and traditional ML models into existing reporting solutions. • Own the end-to-end AI/ML lifecycle: problem definition, data exploration, model development, validation, deployment, and monitoring. • Develop forecasting models, segmentation approaches, and optimisation algorithms to drive sales strategy, and build early warning systems to identify risks and opportunities. • Drive interoperability with existing ML systems and support downstream applications such as dashboards, web tools, and chat interfaces. • Partner closely with engineering, sales ops, and business stakeholders to embed context-aware intelligence into decision-making tools and processes. • Lead technical decision-making on infrastructure, embedding safety mechanisms such as grounding checks and model monitoring. • Conduct experiments and causal analyses to evaluate the impact of business initiatives, and communicate findings clearly to technical and executive audiences. • Contribute to hiring, mentoring, and engineering best practices in model governance, reproducibility, and data quality. • Champion innovation by staying abreast of the latest advancements in AI/ML and actively seeking opportunities to apply new tools and techniques. Minimum Qualifications • Proven years of experience in MLOps, data engineering, or software development, with a recent focus on GenAI, LLMs, and advanced analytics. • Understanding of software engineering practices such as version control, CI/CD containerisation and monitoring, particularly within ML or MLOps context. o Proficiency in Python and/or R, with experience in ML libraries (scikit-learn, TensorFlow, PyTorch) and frameworks such as FastAPI, LangChain, or similar. o Hands-on experience with LLM APIs, foundation models, embeddings, vector databases, RAG workflows, and agentic AI systems including

Key Responsibilities

1. Implement and optimize ML pipelines using MLflow, Kubeflow Pipelines, and TFX, enabling automated model training, validation, and deployment.
2. Integrate DevOps practices with Python scripting to automate infrastructure provisioning via Terraform, AWS CloudFormation, and Ansible for scalable ML environments.
3. Configure and maintain CI/CD workflows using Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline code integration and deployment for ML projects.
4. Monitor and analyze ML system performance using Prometheus, Grafana, ELK Stack, and Fluentd, ensuring reliability and rapid issue resolution.
5. Apply advanced proficiency in Git, GitHub, GitLab, and Bitbucket for source code management and collaboration within the development team.
6. Participate in technical reviews, contribute to process compliance, and support feasibility studies by evaluating technical alternatives and risks for ML solutions.
7. Prepare and submit project status reports, collaborating with internal stakeholders to define deliverables and minimize escalation risks.

Skill Requirements

1. Advanced Proficiency In Ml Ops, Including Mlflow, Kubeflow Pipelines, Tfx, And Metaflow.
2. Advanced Proficiency In Devops Tools Such As Terraform, Aws Cloudformation, Ansible, Jenkins, Gitlab Ci/Cd, Circleci, And Github Actions.
3. Advanced Proficiency In Python For Automation, Scripting, And Ml Pipeline Development.
4. Advanced Proficiency In Monitoring And Logging Tools: Prometheus, Grafana, Elk Stack, Fluentd.
5. Advanced Proficiency In Version Control Systems: Git, Github, Gitlab, Bitbucket.
6. Solid Understanding Of Cloud Infrastructure And Deployment Strategies.
7. Solid Ability To Troubleshoot, Optimize, And Maintain Ml Environments.

Other Requirements

1. Optional but valuable:
2. AWS Certified Machine Learning � Specialty
3. - Google Professional Machine Learning Enginee