Staff Quantitative Researcher, Pricing

Accountabilities: Quantitative Pricing Research: Develop, evaluate, and continuously improve pricing models to increase the accuracy and reliability of valuations across alternative assets, particularly trading cards and other collectibles. Predictive Signal Discovery: Identify new data sources and predictive indicators, including market sentiment, sports outcomes, and alternative data, and assess their value through rigorous statistical analysis and backtesting. Model Development and Validation: Build, test, and refine production-grade pricing and forecasting models using appropriate statistical and machine learning techniques. Evaluate model performance under different market conditions and quantify uncertainty. Market Manipulation Detection: Investigate unusual transactions, suspicious pricing patterns, and potential market manipulation. Develop methods to identify anomalous activity and incorporate relevant risk signals into valuation models. Cross-Market Pricing: Design quantitative approaches for comparing prices across fragmented marketplaces, identifying pricing discrepancies, and accounting for inefficiencies in markets where assets may trade at materially different values. Model Selection and Blending: Compare competing pricing models, determine when individual models should be trusted, and develop model ensembles or blending strategies that balance accuracy, robustness, and uncertainty. Production Deployment and Model Routing: Make evidence-based recommendations and decisions about model releases, version upgrades, and routing strategies. Ensure that new approaches demonstrate measurable value before being introduced into production. Research Prioritization: Independently identify high-impact research opportunities, establish priorities, evaluate trade-offs, and allocate effort toward initiatives with the greatest potential to improve pricing performance. Cross-Functional Collaboration: Partner with expert pricing specialists, data scientists, and machine learning engineers to translate research findings into practical solutions and integrate quantitative methods into existing systems. Research Communication: Explain model behavior, pricing movements, statistical findings, and methodological trade-offs clearly to technical and non-technical stakeholders, supporting informed decisions across the organization. AI-Enabled Innovation: Evaluate emerging AI models, tools, and techniques and apply them where they can improve research efficiency, signal discovery, model development, or pricing operations. Tooling and Methodology Development: Create reusable tools, analytical methods, AI agents, and internal applications that strengthen pricing research capabilities and improve the effectiveness of the wider team. Measurable Business Impact: Deliver demonstrable improvements in pricing accuracy, validate useful data sources for production use, strengthen manipulation-aware valuations, and establish evidence-based model selection practices. Requirements: Quantitative Research Experience: Demonstrated experience developing and deploying pricing or forecasting models in production environments, with a strong ability to translate quantitative research into measurable business outcomes. Statistical and Mathematical Expertise: Strong command of statistical modeling, experimental design, backtesting, uncertainty quantification, and predictive analysis, particularly when working with noisy, sparse, or incomplete data. Programming and Data Analysis: Expert-level proficiency in Python and SQL, with the ability to conduct complex analyses, build scalable research workflows, and work effectively with large or fragmented datasets. Machine Learning Knowledge: Familiarity with tree-based models and modern machine learning approaches, including large language models, time-series foundation models, and graph neural networks. The ability to evaluate the strengths and limitations of different techniques is essential. Model Combination and Optimization: Experience combining multiple predictive signals or models using ensemble methods, forecast combination, portfolio construction, or related quantitative optimization techniques. Alternative Data and Market Prediction: Experience using alternative data to predict market behavior, including unstructured information such as text, sentiment, and other non-traditional data sources. Alternative Asset Expertise: Deep knowledge of non-traditional, fragmented, or illiquid asset markets, such as collectibles, trading cards, art, real estate, alternative credit, or similar asset classes. Market Dynamics and Pricing Judgment: Ability to investigate pricing discrepancies, identify market inefficiencies, distinguish meaningful signals from noise, and make evidence-based decisions when models produce conflicting results. Production Engineering Mindset: Experience translating research into dependable production systems, evaluating operational trade-offs, and collaborating with engineering teams to deploy and maintain quantitative solutions. Practical AI Experience: Hands-on experience building AI-powered tools, agents, or internal applications that deliver practical value to users or teams. A willingness to experiment with emerging AI capabilities and integrate useful techniques into daily workflows is important. Research Ownership: Ability to independently identify promising research directions, formulate hypotheses, design experiments, evaluate results, and make sound decisions without requiring extensive supervision. Communication Skills: Strong written and verbal communication skills, with the ability to explain complex quantitative concepts, uncertainty, and technical trade-offs to non-technical audiences. Collaborative Approach: Ability to work effectively with multidisciplinary teams, incorporate expert judgment, challenge assumptions constructively, and align research decisions with broader business objectives. Relevant Professional Backgrounds: Experience in quantitative research or portfolio construction at an investment firm, statistical or cross-market arbitrage, sports betting, prediction markets, credit or insurance underwriting, marketplace pricing, or trust and safety is particularly relevant. Results-Oriented Mindset: A commitment to rigorous analysis, continuous experimentation, and measurable improvements in model performance and business outcomes. Demonstrated expertise and the quality of prior work are more important than a specific number of years of experience. Benefits: Competitive Compensation: Annual base salary ranging from $185,000 to $220,000 USD , depending on experience, location, and other relevant factors. Equity Compensation: Equity grants for all full-time employees, providing an opportunity to participate in the company's long-term growth. Comprehensive Healthcare: Employer coverage of 85% of medical, dental, and vision insurance premiums, with up to 50% coverage for eligible dependents. Health Savings and Flexible Spending Accounts: Access to HSA and FSA options to help manage eligible healthcare expenses. Generous Paid Time Off: A flexible PTO policy designed to encourage employees to take meaningful time away from work. Paid Parental Leave: Parental leave at full salary to support employees and their families. Wellness Allowance: $200 per month to support personal wellness and wellbeing. Home Office Stipend: $100 per month to help cover remote-work expenses. Remote-First Work Environment: Flexibility to work remotely, with access to WeWork spaces when an in-person workspace is preferred. Retirement Benefits: Access to a 401(k) retirement savings plan. Additional Perks: Complimentary DoorDash Pass membership. Professional Autonomy: Significant ownership over research priorities, quantitative methods, model selection, and production decisions. Innovative Technical Environment: Opportunities to experiment with modern machine learning techniques, AI-powered tools, alternative data, and emerging approaches to quantitative pricing. Collaborative Team Culture: Close partnership with pricing experts, data scientists, and machine learning engineers in an environment focused on experimentation, measurable results, and continuous improvement.