Quant Researcher / Trader-Singapore at Polymer Capital

, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

15 Sep, 26

Salary

0.0

Posted On

17 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Transaction Cost Analysis, Market Microstructure Research, Quantitative Research, Python, KDB+/q, Java, Predictive Modeling, Risk Management, Algorithmic Trading, Statistical Analysis, Econometrics, Machine Learning, Inventory Management, Financial Engineering, Execution Analytics, Data Analysis

Industry

Investment Management

Description

Polymer Capital Management is a market-neutral, multi-manager investment platform based in and focused on Asia. Polymer combines established institutional support and deep knowledge of local financial markets with a dedication to discovering and developing the region's best investment talent. Polymer was established in conjunction with PAG, one of the world's largest Asia-focused alternative asset managers, in 2019. Job Responsibilities / Job Brief We are seeking a Quant Researcher / Trader to join the Trading and Execution team, with a focus on transaction cost analysis, market microstructure research, and central risk book strategy development. The successful candidate will work closely with traders, quants, and technology teams to improve execution quality, understand liquidity dynamics, and develop quantitative tools for internalization, risk transfer, and central risk book optimization. This role requires strong quantitative skills, practical commercial judgment, and an entrepreneurial mindset. Key responsibilities include: Develop and enhance Transaction Cost Analysis (TCA) frameworks to evaluate execution quality across markets, brokers, venues, algorithms, and trading styles. Analyze execution performance, including slippage, market impact, fill rates, opportunity cost, implementation shortfall, and post-trade performance. Conduct market microstructure research using tick-level, order book, trade, quote, order, and execution data. Build predictive models for trading costs, short-term price dynamics, liquidity conditions, market impact, and execution risk. Research, develop, and support central risk book strategies, including internal crossing, risk warehousing, inventory management, hedging, and risk transfer between trading desks. Build quantitative tools and monitoring frameworks to support execution decision-making, internalization opportunities, risk monitoring, performance attribution, and CRB-related strategies. Requirements Strong academic background in Mathematics, Statistics, Computer Science, Engineering, Physics, Financial Engineering, or another quantitative discipline from an established institution. Strong quantitative research skills, with experience applying statistical, econometric, optimization, or machine learning techniques to real-world data. Strong programming ability in Python, KDB+/q, Java, or another language commonly used in quantitative research and trading. Good understanding of market microstructure concepts, including liquidity, spreads, market impact, order types, execution algorithms, venue behavior, and short-term price dynamics. Familiarity with TCA, execution analytics, algorithmic trading, smart order routing, or electronic trading workflows is preferred. Entrepreneurial mindset, with strong analytical skills, commercial judgment, attention to detail, and independent thinking. Strong communication skills and ability to explain quantitative findings clearly to both technical and non-technical stakeholders. Good to have Experience developing execution algorithms, market impact models, or liquidity forecasting models. Experience with broker / venue analysis, algo wheel analytics, post-trade TCA, or execution benchmarking. Familiarity with internal crossing, central risk book, market making, inventory optimization, or risk warehousing strategies. Prior financial markets experience, particularly in equities or electronic trading.

Responsibilities

Develop and enhance Transaction Cost Analysis frameworks and conduct market microstructure research to improve execution quality. Research and support central risk book strategies, including internal crossing and risk warehousing, while building quantitative monitoring tools.