Quantitative Analyst (Evergreen - Talent Pool)

This is not an active opening. We're collecting applications for future Quantitative Analyst roles. Tangible is a fast-growing, tech-driven platform providing liquidity solutions to LPs and GPs in the private-markets secondaries space. By combining deep sector expertise with modern infrastructure, we unlock liquidity across private equity, private credit, and real assets, transforming the industry and enabling investors to move faster and smarter. As a Quantitative Analyst , you will act as a bridge between our analyst team and tech team, helping to integrate financial modeling capabilities onto our platform. This role is open globally to full-time contractors who can work standard Central European Time (CET) hours. Bridge Between Research, Product, and Engineering: Act as the bridge between research, product, and engineering teams; grasp the research-to-production lifecycle of how research becomes a prototype, and a prototype becomes a robust, production-grade system; ensure that financial models and research outputs are accurately and effectively implemented within the platform. Financial Modeling Integration: Collaborate with the tech team to integrate advanced financial modeling capabilities into our platform; translate complex financial models into actionable technical requirements for developers; ensure that financial models are accurately and effectively implemented within the platform. Ownership of the Product Development Lifecycle: Operate across the product development lifecycle, where requirements originate with product, flow to research or engineering, timelines and a roadmap take shape, milestones are hit, and the product ships. Cross-Team Collaboration: Act as a liaison between the analyst team and tech team to facilitate effective communication and project execution; collaborate with analytics and management teams to understand data needs and deliver actionable insights. Reporting and Communication: Prepare comprehensive reports to document findings in a professional and concise manner; apply critical thinking to interpret data and clearly communicate implications to stakeholders. A strong research-to-product mindset: someone who wants to build tools and models for an investment product and has solved problems where the data is sparse or unreliable. Minimum MSc, PhD preferred, in a data-intensive science (Physics, Maths, Statistics, Engineering, Machine Learning, or a related discipline). Exposure to financial modelling, whether through professional work, academic study, or personal projects. Strong knowledge of statistical methods and data analysis techniques, paired with the ability to solve problems creatively. Tech stack: strong Python, good level with Bash and the command line, basic knowledge of JavaScript, fluency in Excel and DuckDB, and Git for version control. Excellent critical thinking skills, with the ability to draw actionable insights from data and communicate implications clearly. Strong written communication skills, with the ability to document findings professionally and concisely. Proven collaboration skills, with experience working effectively in cross-functional and remote/distributed teams with tight deadlines. Thrives in a fast-moving startup environment - flexible, proactive, and self-directed.