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. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1