野村控股
Quantitative Strategist
Quantitative Strategist
发布于 1 天前高层管理(VP/总经理/CEO)
Central, HK
高级经验
全职员工
仅现场办公
本科
SQL
Machine Learning
Equities
Market Microstructure
AI 估算 · 100k–200k
香港投行VP级量化岗，技术门槛高，金融行业薪资水平较高。
职位详情
关于这个职位
This role is for a Vice President level Quantitative Strategist at Nomura's Global Markets division in Hong Kong. You will join the Quantitative Trading Strategy team to drive revenue by improving algorithmic trading execution quality through quantitative research, A/B testing, and model development. The position requires deep expertise in data analysis (SQL, Python) and equities market micro structure, with a focus on applying machine learning and AI to trading strategies.
最低要求
Proven experience extracting and analyzing insights from large and complex datasets using SQL.
Proficient in scripting in Python (experience with C++, Matlab, Java and R would be an advantage).
Knowledge and understanding of current and future of trading technologies such as AI and emerging technologies.
Demonstrable ability to implement practical applications of quantitative research.
Excellent communication and presentation skills with excellent command of the English language.
Strong team player with willingness to both teach others and learn new approaches.
University degree in a scientific discipline e.g. Statistics, Mathematics, Physics, Machine Learning or Computer Science.
工作职责
Improve quantitative performance of trading algorithms.
Perform research on trading outcomes to measure contextual efficacy of various trading strategies.
Perform and design A/B tests to evaluate.
Design models to more effectively use trading tactics to achieve strategic performance, and work closely with engineering team to implement the models.
Be involved in algorithmic trading from inception of idea to research to quantitative follow-up.
优先资格
Experience with Algorithmic Trading would be an advantage.
Prior experience in equities trading, algorithmic trading, market making or market micro structure will be highly advantageous.
AI 洞察
优缺点分析
- Work at a top-tier global investment bank with a strong presence in Asia, offering exposure to diverse markets and clients.
- Leverage cutting-edge technology like AI and machine learning in real-world trading applications, enhancing your technical skill set.
- High visibility role with direct impact on trading profitability, leading to strong career progression and compensation.
- High-pressure environment with tight deadlines and fast-paced market changes requiring quick decision-making.
- Requires deep domain knowledge in both quantitative finance and programming
- steep learning curve for newcomers.
- Long working hours are common, especially during market volatility or system rollouts.
- This role is ideal for a highly analytical and mathematically inclined quantitative professional with a passion for algorithmic trading and a desire to work in a demanding but rewarding financial environment.
角色解读
- Advance to senior quantitative strategist or lead a team, overseeing strategy development for multiple regions or asset classes.
- Transition into a trading role (e.g., quant trader) or move to a hedge fund/asset manager with similar quantitative focus.
- Expand into broader technology leadership roles within global markets, such as head of quantitative research or CTO for trading platforms.
- Develop and improve quantitative models for algorithmic trading to enhance execution quality and reduce costs.
- Conduct research on trading outcomes using large datasets to measure the effectiveness of trading strategies.
- Design and implement A/B tests to validate new algorithms and trading tactics.
- Collaborate with engineering teams to deploy models into production trading systems.
- Strong programming skills in Python for data analysis and model development
- knowledge of C++, Matlab, or R is a plus.
- Expertise in SQL for extracting and analyzing large datasets from trading systems.
- Deep understanding of equities market microstructure and algorithmic trading concepts.
- Familiarity with machine learning and AI techniques applied to financial markets.
申请策略
- Tailor your resume to emphasize results (e.g., improve execution cost by X%) and technical depth (e.g., using neural networks for trade classification).
- Network with current or former Nomura quants via LinkedIn to gain insights into the team's culture and priorities.
- Showcase projects where you developed trading algorithms or quantitative models that improved execution quality or generated alpha.
- Emphasize your experience with large-scale data analysis using SQL and Python, including optimization and performance tuning.
- Highlight any hands-on experience with A/B testing and experimental design in a financial context.
- List relevant publications or open-source contributions in quantitative finance or machine learning.
- Strengthen your understanding of market microstructure and order book dynamics through online courses or reading academic papers.
- Practice implementing classic trading strategies (e.g., VWAP, TWAP) and backtesting frameworks in Python.
面试指南
- For technical questions, start with defining the problem and constraints, then walk through your methodology step-by-step, emphasizing data, model choice, and validation.
- For behavioral questions, use the STAR method (Situation, Task, Action, Result) to provide concrete examples of your impact in previous roles.
- For conceptual questions, demonstrate both theoretical knowledge and practical implementation experience, referencing real markets or instruments.
- Explain how you would design an A/B test to evaluate a new trading algorithm for a specific market condition.
- Describe a time you used machine learning to solve a trading or risk management problem. What model did you choose and why?
- How do you handle noisy financial data? Provide an example of a data cleaning or feature engineering step you applied.
- What is your understanding of market impact and how do you incorporate it into algorithm design?
- Talk about a challenging quantitative problem you faced and how you approached it.
匹配度报告
66
综合匹配度
Top-tier investment bank VP quant role with cutting-edge tech, high pay, but demanding hours and limited WLB.
适合人群
This position is best suited for candidates who prioritize career growth, technical skill enhancement, and compensation over work-life balance.
最强匹配
成长发展匹配
最弱匹配
工作生活匹配
薪资福利70
成长发展85
工作生活50
使命价值60
薪资福利匹配
70中等
The position offers competitive compensation typical of VP-level roles at top investment banks, but exact figures are not disclosed. Benefits are likely comprehensive but not mentioned in the JD.
薪资信号未披露 (100K-200K/月)
成长发展匹配
85较高
The role provides strong opportunities for skill development in cutting-edge areas like AI and algorithmic trading. The team encourages knowledge sharing and innovation, but no explicit promotion path is mentioned.
技术前沿前沿/新兴技术
技术栈Python、SQL、C++、MatLab、Java、R、Machine Learning、AI、Algorithmic Trading
业务类型profit_center
工作生活匹配
50较低
Work mode is unspecified but likely on-site in a prime location. The high-pressure environment and potential for long hours limit work-life balance. No explicit WLB signals in the JD.
工作模式未明确
办公地点市区核心地段
加班情况未提及（无法判断）
使命价值匹配
60中等
The role contributes to financial markets efficiency but has neutral social impact. The industry is mature and stable, with moderate innovation focus.
行业发展稳定成熟行业
社会影响中性/一般
创新程度积极采用新技术
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