FinTech Financial Quantitative Analyst

Join Financial Quantitative Analyst TalentCloud

If you possess mastery in any of the roles or skills below, you can apply to this TalentCloud. Once you become an approved Experfy TalentCloud member, you will get exclusive access to jobs and project opportunities from our clients.

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Cloud Description

  • Financial Quantitative Analysts should be able to assess conceptual foundations of a model, model specification, underlying assumptions, limitations, variable selection, underlying data, developmental evidence, documentation
  • Create, implement, and support quantitative models for the trading business leveraging a wide variety of mathematical and computer science methods and tools including hardware acceleration
  • Develop (derivative) pricing models using mathematical finance for valuation including Monte Carlo Methods and partial differential equation solvers
  • Develop Forecasting models using Statistical or ML methods, time-series information
  • Forecast company-specific announcements - earnings, dividends, or other key ratios
  • Forecast collateral-specific information - prepayment rates, default rates, impairments, write-offs, etc.
  • Forecast prices, returns, use to calculate optimally¬†
  • Meet with company officials to gain better insight into the company's prospects and management, and with investors to explain recommendations
  • Develop quantitative financial products used to inform individuals or financial institutions engaged in saving, lending, investing, borrowing, or managing risk
  • Investigate methods for financial analysis to create mathematical models used to develop improved analytical tools or advanced financial investment instruments
  • Define or recommend model specifications or data collection methods
  • Create or apply independent models or tools to help verify results of analytical systems
  • Write requirements documentation for use by software developers. Collaborate in the development or testing of new analytical software to ensure compliance with user requirements, specifications, or scope
  • Identify, track, or maintain metrics for trading system operations
  • Research new products or analytics to determine their usefulness. Provide application or analytical support to researchers or traders on issues such as valuations or data
  • Develop core analytical capabilities or model libraries, using advanced statistical or quantitative techniques
  • Research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss measurement, or pricing models
  • Apply mathematical or statistical techniques to address practical issues in finance, such as derivative valuation, securities trading, risk management, or financial market regulation

Preferred Education

  • Bachelor, Master or Ph.D. degree in STEM, mathematics, finance, computer science, or computer engineering; or finance, or MSc in Financial engineering

Required Skills

  • 3+ years of experience in comparable quantitative modeling or analytics role, ideally in the financial sector
  • Excellent mathematical and modeling skills
  • Broad statistical toolkit including machine learning, econometrics, and non-parametric methods, Experience with statistical techniques in empirical finance
  • Proficiency in C++ including STL, C#, .NET, Java, Python, object-oriented software design, Structured Query Language (SQL), or NoSQL such as Time-series DBs including kdb+, document databases like MongoDB, graph DBs, etc.
  • Stochastic Calculus, Mathematical finance/ programming, and Probability or Statistics/Econometrics/Machine Learning
  • Experience with factor modeling and portfolio optimization
  • Strong programming skills in languages such as Matlab, Python, C++, and C#
  • A problem-solver, adept at dealing with quantitative-based problems
  • Familiarity with Agile methodologies
  • Excellent written and verbal English communication skills

Preferred Skills

  • Experience with complex data architecture, including modeling and data science tools and libraries, data warehouses, and machine learning
  • Knowledge of predictive modeling, statistical sampling, optimization, machine learning, and artificial intelligence techniques
  • Ability to extract, analyze, and merge data from disparate systems, and perform deep analysis
  • Experience designing, developing, and applying scalable Machine Learning and Artificial Intelligence solutions
  • Experience with data analytics tools (e.g., Alteryx, Tableau, MATLAB)