About the Role
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.
Required 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.
Similar Jobs
What you need to know about the San Francisco Tech Scene
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine



