The Client is the world’s first Performance Branding company, partnering with some of the biggest brands in the world to drive business growth through innovative marketing strategies. Their integrated operating model collapses the traditional marketing silos between creative and media, performance and brand, and across media channels. With a full suite of offerings including media, creative, SEO, Lifecycle, Retail Media, Affiliate and Influencer, they’re able to work with brand partners in an integrated fashion, allowing them to align marketing strategies back to core business objectives. Client teams are trained on how to always act as a trusted business partner, acting as a fiduciary to partners needs above our own.
You will have the opportunity to work with iconic brands such as The North Face, Timberland, Movado Watches and Jose Cuervo. Everyone wants to grow and be challenged. It’s a collaborative place made up of small, closely knit and versatile teams that are fast and adaptive to solve problems and build systems.
About the Role: They are in search of an exceptional Machine Learning Engineer to join their accomplished team. In this role, you will take the lead in developing and fine-tuning predictive ML models, with a primary focus on Ad Score and Ad Account Health. You will play a crucial part in delivering actionable insights and solutions to their clients, and your work will be integral to our mission.

Responsibilities include but are not limited to;

• ML Model Development: Lead the development and refinement of predictive ML models, particularly Ad Score and Ad Account Health.
• Data Analysis: Conduct in-depth data analysis to identify trends, patterns, and insights that inform model development and optimization.
• Feature Engineering: Collaborate with data engineers to create and maintain feature engineering pipelines to support model training.
• Model Evaluation: Implement rigorous evaluation methodologies to assess model performance, making necessary adjustments for continuous improvement.
• Deployment and Integration: Work closely with engineering teams to deploy models and integrate them into our products through APIs.
• Collaboration: Collaborate closely with product managers, full-stack engineers, and TPMs to ensure seamless integration of data science solutions into our products.
• Research and Innovation: Stay up-to-date with the latest developments in the field of data science and machine learning, and explore innovative approaches to problem-solving.

Requirements

• Master’s or Ph.D. in a related field with a strong academic background.
• Proven experience as a Data Scientist with a track record of developing and deploying predictive ML models.
• Expertise in machine learning techniques, including but not limited to regression, classification, clustering, and deep learning.
• Proficiency in data manipulation, feature engineering, and model evaluation.
• Strong programming skills in languages such as Python and experience with libraries like TensorFlow, PyTorch, or scikit-learn.
• Excellent communication skills and the ability to collaborate effectively The Client cross-functional teams.
• A passion for continuous learning and staying updated with the latest trends and technologies in data science.
• Strong problem-solving abilities and the capacity to translate complex data into actionable insights.

Tech Stack

• Google Analytics
• Hotjar
• Rollbar
• Azure Portal
• Terraform Cloud

• ML Model Development: Lead the development and refinement of predictive ML models, particularly Ad Score and Ad Account Health.
• Data Analysis: Conduct in-depth data analysis to identify trends, patterns, and insights that inform model development and optimization.
• Feature Engineering: Collaborate with data engineers to create and maintain feature engineering pipelines to support model training.
• Model Evaluation: Implement rigorous evaluation methodologies to assess model performance, making necessary adjustments for continuous improvement.
• Deployment and Integration: Work closely with engineering teams to deploy models and integrate them into our products through APIs.
• Collaboration: Collaborate closely with product managers, full-stack engineers, and TPMs to ensure seamless integration of data science solutions into our products.
• Research and Innovation: Stay up-to-date with the latest developments in the field of data science and machine learning, and explore innovative approaches to problem-solving