Product Manager, Applied Machine Learning - 25-02367

Job Title: Product Manager, Applied Machine Learning

Location: Remote

Duration: 6+ months/CTH

Pay rate range: $65-$ 74/hr on W2

Job diva ID: 25-02367

Experience:

Product Manager for Applied Machine Learning or Sr. Product Manager. Someone who has managed multiple workstreams, can describe the business problem, can break it down and what the resolution is.

Experience in ML engineering, MLOps, or ML product management, (with a proven track record in Vertex AI and cloud-based ML deployment)

Machine Learning & MLOps: Strong expertise in managing the ML lifecycle, from training to production deployment, using automated pipelines.

Cloud & Infrastructure: Extensive experience with Google Cloud Platform (GCP), especially Vertex AI, including managed pipelines, feature store, model monitoring, and AutoML.

Automation & Scalability: Expertise in scaling ML models using CI/CD pipelines, containerization (Docker, Kubernetes, Vertex AI Pipelines), and model versioning.

Software Engineering & DevOps: Knowledge of infrastructure as code (Terraform, Helm), microservices, and API-driven model deployment.

Performance Monitoring & Adaptation: Experience with Vertex AI Model Monitoring, Cloud Logging, and A/B testing to ensure continuous model effectiveness.

1. Job DescriptionThis team will play an integral part in advancing our advertising solutions by implementing Machine Learning models into our Ad Serving Process to better meet shopper and advertiser needs. This includes developing an automated and scalable pipeline to leverage machine learning algorithms, alongside Data Science and Engineering teams.
2. This is a long-term effort with multiple phases. It provides diverse opportunities for team members to learn and grow with the work. The systems involved are key revenue drivers for the organization. Team members could work in a collaborative environment while delivering impactful capabilities.

1. Responsibilities include:Develop & manage a short/long term roadmap & strategy to scale machine learning models in production through an automated pipeline
2. Understand & communicate business objectives and current technical infrastructure to ensure deployments are scalable and stable
3. Outline customer / business problems and technical challenges associated with the scope of work to aide in discovery and development
4. Contribute to / lead discovery & inception sessions to assess scope of work
5. Utilize data and understanding of business objectives, stakeholder feedback, and technical dependencies to prioritize work across deploying, monitoring, and maintaining pipeline
6. Maintain relationships with stakeholders to instill open lines of communication to share feedback, learning, and objectives
7. Coordinate releases with core teams to ensure a seamless deployment that is on time and meets requirements
8. Continuously monitor performance to ensure models continue to meet KPIs over time and adapt, as needed
9. Lead requirements gathering and management of backlog, alongside development team