Head of Data (Data Science and Data Engineering)

Location: New York Office; San Francisco Office

Department: 13001 - Engineering

The Opportunity

Aircall is a place where voices are valued. Data is the backbone of how we make decisions, powering the products, insights, and customer experiences that drive our revenue and profitability. As our Head of Data, you will lead both our Data Science and Data Engineering teams and set the vision for how Aircall uses data and AI to grow. You will own the full data lifecycle, from the platform and pipelines that make data trustworthy to the models, experiments, and insights built on top of it, and you will build a world-class data organization along the way.

Key Responsibilities:

• Define and execute Aircall’s end-to-end data strategy across data science, analytics, and data engineering, tied to company goals and measurable business impact.
• Lead, mentor, and grow the Data Science and Data Engineering teams, including hiring, career development, and a culture of ownership.
• Set standards and best practices for data quality, governance, and security across both teams.
• Own the data platform architecture (pipelines, warehousing, orchestration, and reliability) so data is trusted, scalable, and accessible across the company. 
• Partner up with the product and engineering teams on feature launches, A/B testing and rapid experimentation.
• Partner with the GTM teams to land accurate numbers of ARR forecasting, customer segmentation, segment targeting and enhancing sellers/AMs day to day productivity with AI enhanced insights on prospects and existing customers
• Keep the team current on emerging research and technologies, and apply them to product design and development.
• Communicate insights and recommendations clearly to stakeholders at every technical level, up to the executive team, and build trust by addressing their real needs.

Qualifications:

Required

• 10-15 years of experience in data science and data engineering, including 5+ years in people leadership.
• Experience leading leaders (managers or senior individual contributors) and owning a team across product and GTM data science and data engineering.
• Track record of setting and executing a multi-year data strategy that earned executive support and delivered measurable business impact.
• Hands-on understanding of modern data infrastructure: pipelines, warehousing, orchestration, and cloud data platforms.
• Experience deploying ML or AI systems to production at scale.
• Technical credibility across data science and data engineering, with a solid grounding in statistics, machine learning, and causal inference, enough to guide architecture, hiring, and prioritization decisions.
• Working proficiency in SQL and Python.
• Ability to communicate data concepts clearly and concisely to executives and cross-functional partners.

Preferred

• B.S, M.S. or PhD in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or another quantitative field.
• Knowledge of NLP, LLMs, or Bayesian testing.
• Coachable, resilient, and solution-oriented, with a bias toward action and a love of a good challenge.
• Prior startup experience