QNB Finansinvest: The symbol of the knowledge, experience and success in the world of investment
Established by QNB Finansbank in 1996, Finansinvest has been continuing to act with its experienced staff and its accumulation of knowledge which has been acquired throughout the years. Intermediary services to its individual and corporate, domestic and foreign customers, portfolio management, investment consultancy, asset management, institutional sales, investment banking and custodian services are among the services provided by QNB Finansinvest to its customers.
Established by QNB Finansbank in 1996, Finansinvest has been continuing to act with its experienced staff and its accumulation of knowledge which has been acquired throughout the years. Intermediary services to its individual and corporate, domestic and foreign customers, portfolio management, investment consultancy, asset management, institutional sales, investment banking and custodian services are among the services provided by QNB Finansinvest to its customers.
Minimum requirements
- Ongoing Bachelor of Science education in Engineering, Business Administration, Economics or a similar quantitative dicipline at top-tier universities.
- Strong Python coding skills
- Good command on Pandas, Numpy, Matplotlib, BeautifulSoup
- Database skills, familiarity with database design patterns.
- A good understanding of time-series analysis, pattern recognition, NLP, statistics, supervised/unsupervised machine learning and reinforcement learning concepts
Preferred skills
- Strong analytical and problem-solving skills
- HTML and CSS for reporting and preparing bulletins
- Curious about financial markets and stock markets
- Knowledge of econometric models
Responsibilities
- Work with the project manager, identify and fix bugs
- Help build, manage and support our applications
- Visualizing, cleaning and manipulating data
- Data driven desicion making and create solutions
- Backtest and implement trading models and signals in a live trading environment
- Conduct research and statistical analysis to build and refine monetization systems for trading signals.
- Visualizing backtest results, economical and stock market data.
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