Quantitative Analyst C2
FNB
Johannesburg, Gauteng, South Africa
3d ago

about us

  • make a promise
  • be deeply invested
  • value our differences
  • build trust, not territory
  • have courage
  • always do the right thing
  • stay curious and you have one simple goal : to improve the lives of customers through simple, effective solutions that meet their needs, contact us today and join a winning team.
  • All appointments will be made in line with the Bank’s Employment Equity plan The Bank supports the recruitment and advancement of individuals with disabilities.

    In order for us to fulfill this purpose, candidates can disclose their disability information on a voluntary basis. The Bank will keep this information confidential unless we are required by law to disclose this information to other parties.

    purpose

    To plan, build, optimise and implement innovative quantitative analytical methodologies, procedures, products and advanced mathematical models that provide analytical support and interpret insights, using advanced analytics technologies, to address business opportunities and problems and implement business strategy.

    experience and qualifications

  • Minimum Qualification - B Degree Maths, Stats, Engineering, Computer Science, Econometrics, Physics or Actuarial Science
  • Preferred Qualification - Honours Degree
  • Experience - 3 - 5 years’ experience in a data environment, of which 1 - 2 years ideally at a at junior (entry level) management level
  • Additional Knowledge - Deep domain knowledge with regards to financial services : Credit, Pricing, Marketing, CVM, Trading etc.
  • Design thinking
  • Analytics Ops, Agile and SAFe concepts will assist
  • Concepts such as : Exploratory data analysis, Data Science Pipeline lines
  • Hands on experience using model such as : Naïve Bayes, Support Vector Machines, Classifications, Boosting Algorithms, Time Series, Feature Engineering and
  • Dimensionality Reduction
  • Data and Information Management topics e.g. structure, dimensions, storage
  • Object-oriented programming
  • Big data modelling
  • Database management
  • Python, SQL, MATLAB, SAS, S-PLUS or R (used for statistical analysis)
  • Monte Carlo techniques
  • Machine learning
  • Data mining and data modelling
  • C++ (used for high-frequency trading applications)
  • Scala and Spark
  • C# / Java, .NET or VBA, Excel
  • Mathematical skills
  • Calculus (including differential, integral and stochastic)
  • Linear algebra and differential equations
  • Numerical linear algebra
  • Probability and statistics
  • Game theory
  • Portfolio theory
  • Equity and interest rate derivatives, including exotics
  • Systematic and discretionary trading practices
  • Credit-risk products
  • Financial modelling
  • Data visualisation and reporting
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