Data Scientist

Signature Aviation is seeking a Data Scientist to join the company’s Operations Intelligence team. As the operator of the largest network of fixed base operators (FBOs) in the world, Signature sits at the center of the aviation ecosystem with access to complex operational and commercial datasets. This role will help unlock the value of that data through advanced analytics, predictive modeling, and data-driven insights that improve operational performance and commercial outcomes across the enterprise. As a Data Scientist, you will work closely with operations, commercial, and technology teams to develop statistical models and analytics solutions that support forecasting, workforce planning, operational efficiency, and revenue optimization. You will own the end-to-end analytics lifecycle, including data preparation, model development, evaluation, and communicating insights to business stakeholders in a clear and actionable format. Minimum Education and/or Experience: 1–3 years of experience in a data science, machine learning, or advanced analytics role Undergraduate degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or another quantitative field Experience applying statistical modeling and machine learning techniques to real-world business problems Additional knowledge and skills: Strong programming skills in Python (e.g., Pandas, scikit-learn) and SQL Experience with time series forecasting and machine learning models Familiarity with data visualization and business intelligence platforms such as Power BI or Tableau Experience applying diverse analytical methodologies to solve technical and operational challenges Ability to communicate complex analytical concepts clearly to both technical and non-technical stakeholders Strong collaboration skills with the ability to work both independently and within cross-functional teams Translate business challenges into analytical approaches and technical solutions Develop statistical and machine learning models supporting initiatives such as demand forecasting, staffing optimization, and operational planning Build time series models and predictive analytics solutions to support operational and commercial decision-making Prepare, clean, and transform raw data into structured datasets suitable for statistical modeling and analysis Identify and incorporate new data sources and variables to improve model performance and predictive accuracy Translate model outputs into operational insights and measurable business KPIs Develop visualizations and reporting tools that communicate model results and insights to business stakeholders Integrate analytical outputs into operational workflows and decision-making processes Partner with cross-functional teams across operations, technology, and analytics to prioritize and execute data initiatives Build relationships with internal and external data and technology partners to enhance analytics capabilities