Data Scientist
Hybrid · New York Office · Full-time · Information Technology
$140,000 – $180,000 per year
Posted · Listed from Lever
About the job
About the role The Data Engineering team at Aircall works on providing high-quality, reliable, and actionable data. As an AI-first data team, we are currently in a pivotal transition to build a robust semantic layer that will power our AI-first data platform, enabling analytics at speed and democratizing intelligent insights across the company. Some of the key problems we are currently solving include taking charge of data reliability, integrating new sources for raw data ingestion, and building sophisticated data models to power real-time dashboards and predictive analytics. In this role, you will be instrumental in building new datasets for high-impact use cases such as churn prediction and feature adoption, while owning the end-to-end reliability and scalability of our data pipelines. You will work closely with Product and GTM business teams, sitting at the heart of a larger data organization alongside Data Science, Analytics, and Applied Scientists to bridge the gap between raw data and AI-driven decision-making.
Your missions @ Aircall:
- Partner with GTM stakeholders to translate business questions into reliable, scalable data products.
- Provide actionable insights and compelling narratives to influence major decisions at the C-level.
- Build and own reporting in Looker, from LookML development to dashboard performance optimization, powering self-service analytics GTM teams rely on every day.
- Work closely with data engineers to continuously improve the data stack, governance practices, and analysis quality.
A little more about you:
- 2+ years of hands-on experience in a high growth environment
- Proficiency in SQL for deriving insights
- Proficiency in Python
- Hands-on Looker/LookML experience
- Excellent interpersonal skills and the ability to explain complex data clearly to stakeholders at all levels
- You have an insatiable curiosity and are biased toward action
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