Quantitative Analyst (Client Solutions)

Responsibilities • Help clients translate our data into actual strategies, working on portfolio construction, backtesting approaches, signal generation, and turning raw forecasts into something tradeable.

• Serve as a primary technical point of contact for our quantitative clients, fielding questions on data quality, methodology, coverage, and how to work with our datasets.

• Write and publish research articles, data notes, and worked examples that showcase the value in Intropic's data to a technical audience.

• Build reproducible examples and analysis in Python to support, illustrate and aid in your research process.

• Investigate and resolve data-related issues clients raise, working with our research and engineering teams to get to the truth quickly.

• Bring client feedback back to our product and research teams to help shape their priorities.

Requirements • Strong working proficiency in Python and particularly Pandas. Comfortable working in Jupyter notebooks, with hands-on experience on data science projects.

• Bachelor's or Master's degree in a STEM subject from a top university (preference towards Mathematics, Physics, Engineering, Economics or Finance), with a solid quantitative foundation built through study and/or work experience.

• Genuine interest in finance and capital markets, and the ability to learn new concepts fast.

• Excellent written communication: able to explain a technical result clearly to a sophisticated audience and produce publishable research.

• Meticulous attention to detail and sound judgment when working under time pressure or with incomplete information.

Nice to Have • Experience within quantitative finance, either through a sales, trading or a research role.

• Familiarity with index methodologies (MSCI, S&P, FTSE etc.) and passive fund mechanics.

• Active use of GitHub and version control; exposure to backtesting or research frameworks.

• Prior familiarity with SQL and navigating sprawling data sets across platforms.

• Prior experience in a client-facing, research, data science or sales-engineering role at a financial data or trading firm.