Product Data Scientist

Nearly all disease will become treatable in our lifetimes, and drug discovery is quickly becoming an engineering discipline. Mandolin is building the “last-mile” delivery infrastructure that gets cutting-edge biologics, cell, and gene therapies to patients faster. Our AI-powered knowledge-worker platform already serves leading infusion clinics, with payers and pharma next in line.

We’re backed by Greylock, SignalFire, Maverick, and founders of famous companies like Yahoo, and led by repeat and exited founders with a team hailing from some of the most technically impressive companies.

We are seeking a Product Data Scientist to be the primary analytical partner for our Product and Business Operations teams. This role’s core responsibility is rapidly answering product and customer questions and investigating incidents by expertly navigating and parsing our complex data structures. This role requires strong analytical rigor, a deep understanding of product dynamics, and the ability to translate complex data findings into clear, actionable insights for stakeholders. The focus of this position is heavily on analytics and partnering with Product, BizOps, and other internal teams.

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  Serve as the single source of truth for product module metrics, partnering with Engineering, Product, and Deployment teams to support internal reporting, customer adoption, and billing needs.

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  Perform deep-dive analyses and generate ad hoc queries and reports to address urgent customer issues and incidents.

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  Support data architecture and analytics design initiatives, such as the implementation of product data capture and key data flows.

• Expert proficiency in SQL for performing ad hoc analysis, developing clean schemas, and parsing complex data structures.

• Strong proficiency in Python for scripting, including cleaning and manipulating complex data, and working with APIs.

• Experience working with large-scale data platforms, specifically BigQuery in a Google Cloud Platform (GCP) environment.

• Familiarity with data ingestion and replication concepts (e.g., Fivetran) and experience working with schema-less data from sources like MongoDB.Proven ability to partner effectively with Product, Engineering, and Business Operations stakeholders to drive outcomes.

• A strong commitment to data compliance and privacy best practices, including awareness of handling and removing PHI.

• The initial focus is heavily on product analytics, but over time, this role offers opportunities to contribute to:

• Analytics Engineering & Data Engineering: Assisting with the implementation of custom data pipelines.

• Experimentation & AI Evaluation: Engaging in advanced analysis related to LLM logging and evaluation and providing key analytical review for ML/AI projects.