Senior Data Scientist, GTM (South America/LATM)
@ Qdrant

Hybrid
Hybrid
Full Time
Posted 3 days ago

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About Senior Data Scientist, GTM

Qdrant is an open-source, high-performance AI search engine and vector database designed to power AI-driven applications. This role is focused on owning and evolving our data infrastructure, with emphasis on data warehouse management, product analytics, and GTM operations.

Key Responsibilities

  • Own and maintain a centralized data warehouse (Snowflake) for reliability and scalability.
  • Design robust pipelines for capturing and structuring product usage data.
  • Enable GTM systems by syncing data with Salesforce, HubSpot, and other tools.
  • Create self-serve dashboards and custom reports using Metabase.
  • Drive analytics around PLG and PQL motions for improved growth and retention.
  • Collaborate closely with Product, Engineering, Sales, Marketing, and GTM teams.
  • Implement data governance best practices and ensure data quality.
  • Support customer facing product alerts and recommendations based on telemetry.

Requirements

Minimum 5 years of experience in data science, analytics engineering, or similar roles. Strong grasp of PLG and PLS motions, deep proficiency in SQL and modern data stacks (dbt, Snowflake, Segment, etc.) coupled with the ability to work cross-functionally.

Benefits

  • Work on cutting edge vector search and AI infrastructure.
  • Join a fast-growing, well-funded startup with a vibrant open-source community.
  • Competitive salary and remote-friendly work environment.
  • Be part of a forefront company in AI and vector search technologies.

Key skills/competency

Senior Data Scientist, GTM, Data Warehouse, Product Analytics, SQL, Snowflake, GTM Operations, Data Pipelines, BI, Data Governance

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How to Get Hired at Qdrant

🎯 Tips for Getting Hired

  • Customize your resume: Highlight data science and GTM experience.
  • Emphasize SQL skills: Detail proficiency in modern data stacks.
  • Showcase collaborative projects: Include cross-functional initiatives.
  • Prepare for technical assessments: Review data pipelines and analytics.

📝 Interview Preparation Advice

Technical Preparation

Review SQL and data warehouse operations.
Practice building data pipelines using Snowflake.
Familiarize with BI tools like Metabase.
Study modern data stacks and dbt usage.

Behavioral Questions

Describe a cross-functional project you managed.
How do you handle tight deadlines?
Explain your approach to data quality challenges.
How do you adapt to changing business needs?

Frequently Asked Questions