Data Scientist

Who We Are

The Data Science Marketing team at Wayfair develops machine-learning algorithms to drive business impact across a multitude of marketing channels - Paid Search, Display & Social Ads, Direct Mail, Email Marketing and Push Notifications to name a few. We partner closely with our Marketing and Engineering counterparts in order to uncover new opportunities and implement large scale solutions that help us connect with a wide range of new and returning customers.

The Data Science Marketing team is looking for a Senior Data Scientist to join the Paid Search team to help optimize paid search ad copies by curating ad text, imagery, and tags.

What You'll Do

• Responsible for the data science components of our Paid Search Ad Copy generation and optimization pipeline
• Develop quantitative models, leveraging natural language process (NLP), machine learning, and advanced statistical analysis techniques
• Own the full Data Science life-cycle from conception to prototyping, testing, deploying, and measuring its overall business value
• Tightly integrate and jointly optimize with more mature data science and engineering products related to paid search ad bidding and customer targeting
• Coordinate, prepare, launch, and assess live experiments in order to measure the incremental imapact of your work
• Uncover deep insights hidden in our vast repository of raw data, and provide tactical guidance on how act on findings
• Leverage your work in order to increase adoption across the organization, to drive real business value

What You'll Need

• 2+ years of experience in a quantitative or technical work environment, or an advanced degree (PhD) in quantitative field (e.g. mathematics, economics, computer science, engineering, physics, neuroscience, operations research etc.)
• Ability to effectively partner with cross-functional leads: strong communication skills, ability to synthesize conclusions for non-technical experts and desire to influence business decisions
• Ability to thrive in a dynamic environment where there can be degrees of ambiguity
• Good intuition of how quantitative and technical work aligns closely with business priorities and business value
• Machine Learning experience in a professional or advanced academic setting (e.g., supervised/unsupervised learning, recommendation systems, reinforcement learning, deep learning, etc.)
• Experience with Natural Language Processing tools and tehcniques in a professional or advanced academic setting
• Fluency in Python and SQL
• Experience with big data tools such as Hadoop, Spark, Presto, etc.
• Bonuses: experience with GCP, Linux, or Git; experience with visual embeddings or computer vision