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 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.
- 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.
- 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.
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