Staff Data Scientist

About the Role

CharterUP is seeking a Staff Data Scientist to advance our data science capabilities within a growing marketplace team. You’ll be a hands-on technical leader, designing and deploying machine learning models in production with an immediate focus on pricing and availability optimization that directly shapes customer experience and marketplace efficiency. Over time, you’ll expand into experimentation, model automation and optimization, while helping strengthen the systems, standards, and practices that enable data science to scale across the organization. This role combines deep individual technical ownership with the opportunity to meaningfully influence how data informs decision-making across CharterUP’s platform and operations.

Title: Staff Data Scientist

Reports to: Chief Product Officer

Location: This position is based in Austin, TX (Hybrid: Monday, Wednesday, Friday in-office).

What You’ll Do

• Hands-On Model Development: Design, implement, and maintain production ML models with an immediate focus on pricing and availability, expanding into forecasting, automation, and optimization
• Build and Shape the Function: Establish the principles, infrastructure, and systems for data science, ensuring a scalable foundation that supports long-term growth.
• Data Infrastructure & Quality: Drive improvements in data pipelines, modeling, and governance to ensure reliable, high-quality data for decision-making.
• Strategic Partnering: Collaborate closely with executives and cross-functional teams to translate complex data into clear insights, metrics, and recommendations that influence business direction.
• Champion a Data-First Culture: Promote best practices in experimentation, statistical rigor, and data storytelling, while mentoring peers and setting the stage for a future team.

Key Initial Objectives (6-12 months)

• Translate pricing and availability insights into scalable systems: Work with Engineering to embed elasticity models, demand forecasts, and operator availability logic into the core platform so they directly influence quotes and surfaced options.
• Build production-ready pricing and matching levers: Design and implement dynamic rules or models that optimize both customer price acceptance and operator match rates, ensuring supply is surfaced efficiently and profitably.
• Establish robust testing and monitoring: Put in place frameworks for controlled experiments on both pricing and supply-matching, with clear KPIs that balance margin, conversion, and operator acceptance.
• Accelerate decision-making with transparency: Ensure pricing and availability strategies are explainable and measurable, with dashboards that make outcomes visible across Product, Sales, Finance, and Ops.
• Lay the foundation for scale: Develop processes and systems that allow pricing and operator matching to scale beyond one-off analysis, creating a repeatable, trusted capability within the organization.

What You’ll Bring

• Proven Data Science Leadership: 8+ years of experience in data science with a track record of building and scaling data-driven functions, leading cross-functional teams, and delivering high-impact projects in high-growth or startup environments.
• Machine Learning & Statistical Expertise: Hands-on experience designing, deploying, and maintaining ML models in production, including pricing, demand forecasting, and availability optimization, paired with deep skills in AI/ML, LLMs, NLP, statistical modeling, experimental design, and advanced analysis.
  Data Infrastructure & Technical Depth: Advanced proficiency with SQL in big data environments, Python/R for large-scale analysis, and strong background in data modeling, pipeline development, and ensuring data quality and governance.
• Strategic and Business Alignment: Demonstrated ability to align data science initiatives with business goals, translating complex data into clear strategies and actionable insights that drive growth and operational efficiency.
• Collaboration and Communication: Excellent communicator and partner across technical and non-technical teams, with proven ability to influence at the executive level, mentor peers and junior talent, and champion a data-first culture across the organization.

Recruiting Process

• Step 1 - Video call: Talent Acquisition interview
• Step 2 - Video call: Hiring Manager interview
• Step 3 - Video call: Team interviews (x2)
• Step 4 - Video call: Case Study & Panel Presentation
• Step 5 - Offer & reference check
• Welcome aboard!