[Hiring] Senior Data Engineer @Redhorse Corporation

Role Description

Redhorse Corporation is seeking a Data Scientist to support Condition-Based Maintenance Plus (CBM+) initiatives to enhance aircraft sustainment and optimize maintenance and logistics efficiency. The Data Scientist will help provide quality data and develop predictive maintenance and logistics forecasting models to support decision-making tools for program-wide initiatives serving U.S. Air Force (USAF) stakeholders.

This role involves working within cross-functional teams in an Agile development environment. We operate in a fast-paced, evolving domain, and are seeking creative, motivated, and talented individuals who are eager to learn, grow, and deliver effective, high-impact solutions.

Key Responsibilities

• Support CBM+ program initiatives from inception to deployment, ensuring alignment with overarching business and mission objectives.
• Oversee end-to-end data collection and processing, including the implementation, sustainment, and optimization of ETL pipelines.
• Perform Exploratory Data Analysis (EDA), statistical analysis, and data visualization to identify trends, correlations, and actionable insights that drive product development.
• Build and maintain knowledge graph capabilities.
• Collaborate with multi-functional engineering teams to integrate trained models into production applications and APIs.
• Continuously monitor data quality and model performance to detect and mitigate drift and degradation.

Qualifications

• Active U.S. Government Secret Security Clearance (U.S. Citizenship required; applicants without an active Secret Clearance cannot be considered).
• Bachelor’s degree in a STEM (Science, Technology, Engineering, or Mathematics) field or proven equivalent professional experience.
• NLP Experience: 2+ years of experience incorporating applied Natural Language Processing (NLP), data labeling, and entity or keyword extraction.
• Analytics & Data Engineering: Proven proficiency with Databricks, PySpark, SQL, data governance frameworks, and technical documentation standards.
• Statistical Foundations: Deep understanding and practical application of statistical distributions for data assessment, data analysis, and predictive modeling.
• Collaboration & Self-Direction: Demonstrated self-starter capabilities with strong interpersonal skills to foster positive stakeholder relationships and execute tasks independently.
• Communication: Excellent verbal and written communication skills, with the ability to translate complex technical findings for non-technical and executive audiences.
• Tools: Experience with project management and productivity platforms including Jira, Confluence, Lucidchart, and Microsoft Office Suite (Word, Excel, PowerPoint). Experience with data end products such as Qlik Sense, Streamlit and other visualization or dashboard tools.
• Travel: Ability to travel, minimal, as needed to engage with government customers and project stakeholders.
• Growth: Possess a desire to learn and advance skills in the aviation data space.

Preferred Qualifications

• Master’s degree in a STEM field, with a preference for Data Science, Data Analytics, or Computer Science.
• Advanced expertise in applying artificial intelligence and machine learning (AI/ML) to predictive maintenance and defense logistics challenges.
• Direct experience developing sensor-based failure prediction models and equipment health indicators.
• Practical experience conducting Monte Carlo simulations and calculating statistical confidence intervals.
• Demonstrated expertise in end-to-end data pipeline engineering including customer-facing UI/UX integration.

Benefits

• $190,000 - $210,000 a year.
• Actual compensation will be determined based on a variety of factors, including relevant experience, education, certifications, skills, security clearance level, geographic location, market conditions, and internal equity.
• Eligible employees may participate in Redhorse's comprehensive benefits programs and may be eligible for performance-based or other incentive compensation, where applicable.