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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Aircall - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $140,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Information Engineering, Python (Programming Language), Performance Tuning, Raw Data, Looker Analytics, Data Pipelines - **Published:** July 12, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a74409caf98e1d1a ## About the Role * 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 ## Description 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., * 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., We pride ourselves on promoting active inclusion within our business to foster a strong sense of belonging for all. We're working to create a place filled with diverse people who can enrich and learn from one another. We're committed to ensuring that everyone not only has a seat at the table but is valued and respected at it by providing equal opportunities to develop and thrive. We will constantly challenge ourselves to make sure that we live up to our ambitions around diversity, equity and inclusion, and keep this conversation open. Above all else, we understand and acknowledge that we have work to do and much to learn. Want to know more about candidate privacy? Find our Candidate Privacy Notice here. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Why Your AI Agent Keeps Hallucinating Your Data: Building Deterministic Context Layers](https://www.wearedevelopers.com/videos/2055-why-your-ai-agent-keeps-hallucinating-your-data-building-deterministic-context-layers) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [The Innovation Formula: Fast Prototyping, Data Analysis, and Real User Insights](https://www.wearedevelopers.com/videos/1421-the-innovation-formula-fast-prototyping-data-analysis-and-real-user-insights) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)