Wayfair’s Data Science team builds the algorithmic systems that drive their business, enhance customer experience, & improve customer loyalty. The Marketing Data Science team at Wayfair develops machine learning models and reinforcement learning systems to power algorithmic decision-making across paid and owned media and marketing channels, including Paid Search, Display & Social Ads, Direct Mail, Email Marketing and Push Notifications.
They partner closely with their Marketing, Ad Tech, and Engineering to build scalable algorithmic systems responsible for optimizing millions of customer-level decisions each day: Who do they target? On what channels? How frequently? How much do they bid? What type of ad do they show? Which creative asset? etc.
They are looking for a Senior Data Science Manager to join the Data Science Marketing team working on their algorithmic marketing platform, with an emphasis on developing scalable Reinforcement Learning algorithms and/or constrained optimization problems. You will be processing petabytes of first party and third party clickstream data to build customer-centric ML models and power high-frequency, customer-level, decision-making systems. This role will be highly cross-functional and working across paid and owned media. When you think about the large number of interrelated decisions their algorithmic system is responsible for, it becomes clear why this is one of their most impactful but also intellectually challenging data science problems at Wayfair. Their algorithms optimize millions of real-time decisions, and they power over $800M worth of advertising spend across the portfolio of channels they support.
What You’ll Do
-
- Responsible for the full Data Science life-cycle from conception to prototyping, testing, deploying, and measuring its overall business value
- Develop quantitative models, leveraging machine learning and advanced data analysis techniques
- Architect and build technical platforms for their algorithmic engines to run at scale
- Leverage their work in order to increase adoption across our business partners, to drive real business value
- Uncover deep insight hidden in our vast repository of raw data, and provide tactical guidance on how to act on findings
- Use data to improve how they make decisions and ultimately, enhance customer experience and drive loyalty
- Strong partnership with business and engineering teams
- Deliver presentations to high-level business stakeholders that tell cohesive, logical stories using data
What You’ll Need
- 4+ years of experience in a quantitative or technical work environment or advanced degree (Ph.D.) in a quantitative field (e.g. mathematics, economics, computer science, engineering, physics, neuroscience, operations research, etc.)
Machine Learning experience (such as supervised/unsupervised learning, recommendation systems, reinforcement learning, deep learning, etc.)
- Bayesian Learning, Multi-armed Bandits, or Reinforcement Learning strongly preferred
- Ability to effectively work with business leads: strong communication skills, ability to synthesize conclusions for non-experts and desire to influence business decisions
- Proficient at one or more programming languages, e.g. Python, R, Java, C++, etc.
- Prior experience building scalable data processing pipelines with big data tools such as Hadoop, Hive, SQL, Spark, etc.
- Experience with GCP, Airflow, and containerization (Docker) are nice to have
- A bias towards solving problems from a customer-centric lens and an intuitive sense for how the work aligns closely with business objectives
- Ability to thrive in a dynamic environment where there can be degrees of ambiguity
- Ability to effectively work with business leads: strong communication skills, ability to synthesize conclusions for non-experts and desire to influence business decisions
