Data Scientist - Airvoyant

We are building the next generation of data-driven aviation software to transform aircraft maintenance and operational performance. Our customers include airlines, MROs, and aviation vendors seeking more efficient, intelligent, and cost-effective solutions to manage complex operations.

We are looking for an experienced Data Scientist to lead the development of advanced analytics, machine learning models, and data products that deliver measurable business impact. This role will be instrumental in converting complex data into scalable solutions, predictive insights, and intelligent automation used by both internal teams and external clients.

You will work cross-functionally with product, engineering, marketing, and business leaders to shape strategy, optimize performance, and accelerate innovation across our platform.

Responsibilities

• Extract, analyze, and interpret complex structured and unstructured datasets to generate actionable insights for internal stakeholders and external clients
• Lead the design, development, validation, and deployment of predictive, statistical, and machine learning models that solve high-impact business problems
• Design and implement data models and algorithms supporting forecasting, optimization, anomaly detection, personalization, and decision automation
• Partner with product, engineering, and business teams to identify high-value opportunities where data science can drive measurable outcomes
• Translate analytical insights into production-grade data products, APIs, and scalable solutions
• Design and execute experiments (A/B testing, hypothesis testing) and deliver clear, data-driven recommendations
• Develop dashboards, visualizations, and self-service analytics tools to democratize insights across technical and non-technical users
• Analyze data to improve product performance, operational efficiency, customer experience, and revenue outcomes
• Evaluate new data sources and data collection methodologies to ensure quality, accuracy, and relevance
• Build frameworks and processes to monitor model performance, drift, and data integrity over time
• Document methodologies, assumptions, and results to ensure transparency, reproducibility, and knowledge sharing
• Communicate insights and recommendations effectively to senior leadership and cross-functional stakeholders