Staff Data Scientist

Grailed is looking for a Staff Data Scientist to drive personalization, recommendation, and product marketplace improvement efforts. The ideal candidate is able to think like a Grailed user as well as a business owner – understanding how data impacts a fashion-forward user experience and also how it is generated and leveraged – while bringing a strong technical background to the role. Specifically, the role requires an understanding of dimension reduction techniques, predictive modeling (statistical or ML), and other advanced analytic methods. This key role will operate at the intersection of Data, Product, Engineering, and Marketing, working cross functionally to develop compelling data products to help buyers find their Grail and sellers maximize their GMV. This role will work with our data in Snowflake, develop models in Python, collaborate with ML engineers to structure data for consumption, and coordinate with Product and business unit leaders to align data product development with business objectives. 

In this role, you will:

• Form a high-level perspective on objectives across departments in the organization and how advanced data methods might solve complex business problems
• Be able to autonomously and proactively identify business problems that could benefit from data solutions, whether it be application of existing models or the need for the development of new model(s), and take ideas through all phases, from proposal to alignment to execution
• Establish best practices for training and development of data models; performance evaluation and monitoring; maintenance, etc.
• Own the deployment of trained models into production in collaboration with Data or ML Engineers
• You will be responsible for ensuring reliable, observable deployment into Snowflake using DBT, integrating with existing data pipelines and platform infrastructure, and maintaining version control of code and configurations via Git
• Evaluate model performance and iterate to improve accuracy and effectiveness. This includes using A/B testing to validate the impact of personalization initiatives and communicating results to stakeholders
• Mine user data to identify opportunities for personalization improvements; this includes defining and tracking KPIs related to personalization effectiveness
• Develop and maintain data models in Snowflake to support analytical and reporting needs, providing insights to business stakeholders across various departments
• Use Python to create ML models and structure the resulting data into a consumable flow
• Develop user-to-user mapping capabilities using graph databases and vector embeddings to enhance personalization
• Utilize search technologies (i.e. Algolia, AWS OpenSearch) to enhance product discovery and personalization
• Analyze message content to detect potentially fraudulent activities, such as identifying keywords or phrases associated with scams, requests for off-platform transactions, or attempts to phish for personal information
• Collaborate with product managers, engineers, designers, and business stakeholders to understand their data needs and provide data-driven solutions

We are looking for: 

• 8+ years of relevant work experience in a data or quantitative role, demonstrated success in a startup, high-growth or faced paced organization
• Graduate degree in data science, analytics, mathematics, machine learning, computer science, or related field is a plus
• Experience in marketplace, e-commerce, or fashion/retail domains preferred
• Experience with web + App product environment preferred
• Demonstrated success in nontechnical, cross-functional partner communication
• Ability to tell a story with data, explaining complex concepts or results to audiences ranging from C-suite to IC levels
• Proven expertise in advanced statistical modeling, causal inference, experiment/test design, and working knowledge of machine learning algorithms
• Expert level proficiency in Python for data manipulation, statistical analysis, and model development
• Practical experience with vector databases and embeddings for tasks like user-to-user or user-to-item mapping, semantic search, or item similarity preferred
• Experience with Snowflake for SQL and data-warehousing preferred
• Experience with DBT for building modular, version-controlled data transformations preferred
• Experience with Git for collaborative code development and review preferred
• Experience in designing, developing, deploying and optimizing Personalization and Recommendation products at scale
• Experience building models to assess item/listing quality (as defined by likelihood of sales), classify listings, and use NLP on unstructured text
• Experience modeling time-series forecasts for market trends, seasonality, demand prediction and other relevant KPIs