Hogarth is the Global Content Experience Company. Part of WPP, Hogarth partners with one in every two of the world’s top 100 brands including Coca-Cola, Ford, Rolex, Nestlé, Mondelez and Dyson. With a breadth of experience across an extensive range of sectors, Hogarth offers the unrivaled ability to deliver relevant, engaging, and measurable content across all channels and media - both established and emerging.
The number of channels at our fingertips; the need for speed; and the drive for mass personalisation, all mean that brands need different solutions.
Our global team of over 7,500 craft and technology experts brings together creative, production and innovation to help clients navigate this exciting and ever-changing world of today’s content experience.
The ideal candidate will possess a deep understanding of building, deploying, and maintaining machine learning models in a production environment. The candidate will be instrumental in driving the development of ML ecosystem on cloud platforms, ensuring seamless integration between various components and continuous monitoring of deployed models to maintain their effectiveness.
Key Responsibilities:
Model Development and Deployment:
Design, build, and deploy machine learning models into production environments. Ensure that ML models are seamlessly integrated into existing systems and processes. Collaborate with data scientists and software engineers to optimize model performance and scalability.
Cloud Platform Expertise:
Leverage cloud platforms (AWS, Google Cloud, Azure, etc.) to build and deploy ML models. Set up and manage cloud-based compute resources, including virtual machines, containers, and serverless architectures, to support ML workloads. Design and implement automated ML pipelines for data processing, model training, and deployment.
Machine Learning Ecosystem Development:
Establish and manage a comprehensive ML ecosystem within cloud platforms, including model registries, version control, and experiment tracking. Develop and expose API endpoints for accessing ML models in production. Implement continuous integration/continuous deployment (CI/CD) processes to streamline ML model updates.
Data Science and Feature Engineering:
Apply advanced data science algorithms and models to solve complex business problems. Perform feature engineering, hyperparameter tuning, and model validation to optimize model performance. Work closely with data engineers to develop and maintain feature stores and data pipelines.
Programming and Database Management:
Develop and maintain ML codebases using programming languages such as Python, R, and SQL. Exposure to relational databases (e.g., MySQL, PostgreSQL) and non-relational databases as required. Collaborate with data engineering teams to ensure smooth data flow and integration between different systems.
Monitoring and Maintenance:
Monitor deployed ML models in production to ensure they perform within defined thresholds. Set up automated alerts and dashboards to track model performance over time. Take proactive measures to retrain or update models when performance degrades, ensuring continued accuracy and reliability.
Generative AI Knowledge:
Set up and maintain environments for Generative AI, integrating these capabilities into the broader ML ecosystem.
Qualifications:
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field. A minimum of 6+ years of experience in building, deploying, and maintaining machine learning models in production. Proven experience with at least one major cloud platform (AWS, Google Cloud, Azure) and proficiency in using cloud-native tools and services. Strong understanding of machine learning algorithms, data science methodologies, and statistical modeling techniques. Expertise in feature engineering, model selection, and hyperparameter tuning. Proficiency in programming languages such as Python, R, and SQL. Knowledge of setting up and managing Generative AI environments is a plus. Excellent communication and collaboration skills, with experience working closely with cross-functional teams including data engineering and analytics teams. Demonstrated ability to monitor and maintain ML models in production environments, with a proactive approach to model management and retraining.
Preferred Qualifications:
Experience with MLOps tools and frameworks such as Kubeflow, MLflow, or TensorFlow Extended (TFX). Familiarity with containerization technologies like Docker and Kubernetes for scalable model deployment. Knowledge of API development and integration for deploying ML models as services.
Diversity & Inclusion
Hogarth is an equal-opportunity employer. That means we believe in creating a truly inclusive culture that values diversity, equity and inclusion for everyone through our ideas, our people, how we behave and how we conduct ourselves. We strive to recruit people from diverse backgrounds and support them to achieve long-term success. This not only makes Hogarth a better company and place to work, but an environment where everyone can give their point of view, experience connection, enjoy opportunity and feel a sense of belonging.
We welcome applications from everyone, regardless of race, ethnicity, religion or belief, gender, gender identity, age, national origin, marital status, military veteran status, genetic information, sexual orientation, or physical or mental disability. As part of our commitment to making our hiring processes as equitable as possible, we are currently rolling out a policy which ensures that hiring managers review CVs only after they have been processed through an automated anonymisation system. This aims to ensure that all candidates are considered for interview based solely on their experience and what they can bring to the role. The solution, provided by MeVitae, scans and redacts CVs to reduce potential reviewer bias.
Please contact careers@hogarth.com if you need the job advert or form in another format.
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