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 intoproduction-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

• Bachelor’s degree in engineering, Mathematics, Statistics, Machine Learning, Analytics, Information Systems, or a related quantitative field; Master’s degree strongly preferred

• 5-7+ years of experience in data science, advanced analytics, or related roles delivering production-grade data solutions

• Advanced proficiency in Python for data analysis and machine learning (pandas, NumPy, scikit-learn, XGBoost, etc.)

• Expert-level SQL skills for complex data querying, transformation, and performance optimization

• Strong experience with machine learning techniques, including supervised/unsupervised learning, feature engineering, and model evaluation

• Solid foundation in statistics, including experiment design, hypothesis testing, and inferential analysis

• Experience with big data technologies and distributed systems (Spark, Hadoop, Hive, Presto, etc.)

• Hands-on experience with containerization and environment management (Docker, Conda) and modern data pipelines

• Proven track record of deploying and maintaining models in production environments

• Experience with cloud platforms (AWS, Azure, or GCP) for data and ML workloads

• Experience with data visualization / BI tools (Tableau, Power BI, or similar)

• Strong communication skills with the ability to translate complex analyses into clear business insights

Preferred Skills and Qualifications

• Experience in aviation, MRO, ERP, or operations-heavy industries

• Familiarity with time-series analysis, predictive maintenance, or reliability modeling

• Experience building real-time or streaming analytics systems

• Exposure to ML Ops practices, including model monitoring, versioning, and lifecycle management

• Experience in integrating models into customer-facing products or APIs

• Hands-on experience with Amazon Redshift for data warehousing, high-performance querying, and large-scale analytics

• Experience building dashboards and business intelligence solutions using Amazon QuickSight (or similar tools such as Tableau, Power BI)

• Familiarity with digital analytics tools (e.g., Google Analytics, Adobe Analytics)

• Understanding of regulated or compliance-driven environments