We are looking for an experienced Machine Learning Scientist II to join the Advertising Optimization and Automation Science team. In this role, you will be responsible for the development of budget, tROAS, and SKU recommendations, and other machine learning capabilities supporting our ads business. You will work closely with other scientists, as well as members of our internal Product and Engineering teams, to apply your engineering and machine learning skills to solve some of our most impactful and intellectually challenging problems to directly impact Wayfair's revenue.
Responsibilities:
- Provide technical leadership in the development of an automated and intelligent advertising system by advancing the state-of-the-art in machine learning techniques to support recommendations for Ad campaigns and other optimizations
- .Design, build, deploy, and refine extensible, reusable, large-scale, and real-world platforms that optimize our ad experience
- .Work cross-functionally with commercial stakeholders to understand business problems or opportunities and develop appropriately scoped machine learning solutions
- .Collaborate closely with various engineering, infrastructure, and machine learning platform teams to ensure adoption of best practices in how we build and deploy scalable machine learning services
- .Identify new opportunities and insights from the data (where can the models be improved? What is the projected ROI of a proposed modification? )
- .Research new developments in advertising, sort and recommendations research, and open-source packages, and incorporate them into our internal packages and systems
- .Be obsessed with the customer and maintain a customer-centric lens in how we frame, approach, and ultimately solve every problem we work on
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Requirements
- : Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related fiel
- d.6-9 years of industry experience in advanced machine learning and statistical modeling, including hands-on designing and building production models at scal
- e.Strong theoretical understanding of statistical models such as regression, clustering, and machine learning algorithms, such as decision trees, neural networks, et
- c.Familiarity with machine learning model development frameworks, machine learning orchestration and pipelines with experience in either Airflow, Kubeflow, or MLFlow, as well as Spark, Kubernetes, Docker, Python, and SQ
- L.Proficiency in Python or another high-level programming languag
- e.Solid hands-on expertise deploying machine learning solutions into productio
- n.Strong written and verbal communication skills, ability to synthesize conclusions for non-experts, and overall bias towards simplicit
y.
Nice to hav
- e: Familiarity with Machine Learning platforms offered by Google Cloud and how to implement them on a large scale (e. g. BigQuery, GCS, Dataproc, AI Notebook
- s).Experience in computational advertising, bidding algorithms, or search ranki
- ng.Experience with deep learning frameworks like PyTorch, Tensorflow, e
tc.
