Company Overview

This is an early-stage, venture-backed technology company developing an AI-native platform for the secondary enterprise hardware market.

The company is modernizing how IT asset disposition companies, brokers, and institutional buyers manage hardware liquidation, compliance, auctions, and pricing.

Their platform combines AI-powered automation, marketplace infrastructure, and advanced data analytics to bring transparency and efficiency to a traditionally fragmented industry.

One of the company's primary initiatives is developing a pricing intelligence engine for used enterprise hardware.

Unlike publicly traded financial instruments, used servers, GPUs, memory, and storage devices have limited standardized pricing data. Tiber is developing models that transform fragmented transaction records and market signals into reliable asset valuations.

Position Overview

They are seeking a Quantitative Researcher to lead the development of its hardware pricing and valuation infrastructure.

This individual will develop models, data pipelines, and analytical systems capable of estimating fair market values and forecasting depreciation across enterprise hardware categories.

The role combines quantitative research, statistical modeling, data engineering, and production software development.

The successful candidate will work closely with the CTO, engineering team, and experienced hardware traders to translate market knowledge into scalable analytical products.

Key Responsibilities

• Design and develop pricing models for used enterprise technology assets.
• Build data pipelines to ingest, clean, normalize, and structure fragmented market and transaction data.
• Develop valuation methodologies for assets with limited historical pricing information.
• Analyze historical transactions, comparable products, substitute assets, and market supply signals.
• Build depreciation curves and residual-value forecasts for enterprise hardware.
• Develop models that account for differences between individual transactions and large wholesale trades.
• Validate statistical models and communicate confidence levels, assumptions, and limitations.
• Incorporate industry expertise and trader knowledge into quantitative models.
• Develop production-ready data infrastructure supporting pricing and analytics.
• Utilize AI tools and agentic workflows to automate data collection, classification, and validation.
• Work with engineering and product teams to integrate pricing models into customer-facing applications.
• Develop interpretable analytical outputs that enable users to understand and trust pricing recommendations.

Required Qualifications

• 4+ years of experience in quantitative research, data engineering, data science, financial modeling, or related technical disciplines.
• Strong statistical and analytical capabilities.
• Experience working with large, fragmented, incomplete, or unstructured datasets.
• Demonstrated ability to develop production data pipelines or analytical models.
• Experience with data ingestion, normalization, and transformation.
• Strong software engineering fundamentals and ability to deploy models or analytical systems into production.
• Ability to develop defensible models using limited observations, comparable assets, or statistical assumptions.
• Experience validating analytical results and distinguishing meaningful signals from noise.
• Familiarity with modern AI development tools.
• Strong problem-solving skills and comfort working independently in ambiguous environments.

Preferred Qualifications

• Experience in quantitative finance, trading systems, asset pricing, or risk modeling.
• Background at a hedge fund, investment bank, exchange, fintech company, or sophisticated data engineering organization.
• Experience building valuation models for illiquid or alternative assets.
• Familiarity with financial forecasting, depreciation models, or market pricing.
• Experience building data platforms supporting trading or financial applications.
• Experience with agentic AI workflows for data collection and validation.
• Interest in enterprise hardware, secondary markets, or physical asset valuation.
• Experience developing interpretable models and data visualizations.