Ishaan N.
96% Job Success
Top Rated Plus

Data Science, Quant Modelling | Finance | Ex-Morgan Stanley, JP Morgan

• Specialist in finance and economics with experience in private equity, investment banking, investment research, corporate valuation and quantitative finance • Proficient in creating quantitative models on Python (Jupyter notebooks) and Microsoft Excel to solve problems and uncover data-driven insights • Organised and systematic, value honest and open communication --------------------------------- PROJECT LIST DATA ENGINEERING AND PROCESSING FRAMEWORKS • Throughout my masters, independent coursework and work experience, I have dealt with projects which required building automated data capturing and processing frameworks • Data processing framework in Python to interact with APIs of data providers like FRED, NASDAQ Data Link, OECD Stat for importing raw data and carrying out several data processing steps like frequency conversion, adjustments for data release dates, units of measurement etc. • I have written Python scripts to automate the job application process. These scripts scrape jobs from websites of target companies, display new vacancies in one place & save them in a database for future reference • I also worked on a dataset of signals on an ETF to assess their effectiveness. To do this, I carried out a number of data pre-processing and pipeline construction steps in Python using a Jupyter notebook environment ECONOMETRICS • I have used ARIMA-GARCH, VARMA-Multivariate GARCH & VECM models to create a trading strategy using Gold, Equity ETF returns & Bitcoin returns • I also used R in a project which required the use of various time series techniques to predict monthly natural gas consumption in Illinois, US MACHINE LEARNING AND PREDICTION • Ran several unsupervised (PCA, K-means) and supervised (CART, SVM, NN) ML algorithms on 35-dimensional ETF dataset • I undertook a causal study of ETF returns across 11 US sectors and 19 economic indicators across 3 buckets (leading, coincident, lagging) using linear and lasso regressions, CART and K-means algorithms. I then classified each of these ETFs into leading, coincident and lagging buckets • This was followed by derivation 165 combinations of efficient 3-ETF portfolios using the Critical Line Algorithm, and their comparison with 3-PC portfolio. I then studied the relationship between returns of the 165 portfolios and weight allocation to leading, coincident and lagging economic indicators • Created an image recognition algorithm which distinguishes cats from ‘non-cats’ • Implemented a two-class classification, 3-layered deep neural network for planar data classification using non-linear activations. Write forward and back propagation algorithms from scratch to fit the DNN’s parameters • Wrote code to apply L2 regularisation with dropout to a deep learning model that recommends positions to football players • Built a gradient checking algorithm to check for any errors in the implementation of a fraud detection deep neural network model • Apply optimisation algorithms like stochastic gradient descent, momentum, RMSProp and Adam to speed up convergence and improve optimisation of a deep neural network’s parameters BACKTESTING AND ANALYTICS • I am testing a variety of trading algorithms on multi-asset portfolios using Python libraries for data manipulation and ML like NumPy, Pandas, Sci-kit Learn etc. • I also run optimisation exercises using Python libraries like SciPy to find optimal levels of that algo's parameters • I then use the Dash framework in Python to build interactive analytics dashboards to display the algo's results for a customisable set of inputs and parameters SIMULATION AND CALIBRATION • Monte Carlo simulations for barrier options pricing, yield curve fitting (NSS) • Construction, testing and validation of a robust model to price a basket of 5 nth-to-default CDS FINANCIAL MODELLING • Equity research report on Associated British Foods (ABF) using Discounted Cash Flow (DCF) and residual income valuation • 3-statement (income statement, balance sheet, cash flow statement) financial modelling and projections for Chewy Inc. • Presentation deck pitching PropTech start-ups offering home ownership and senior housing solutions for millennials and the silver economy respectively • Strategic pitch analysing expansion opportunities across potential European cities for a real estate company offering co-living spaces --------------------------------- INDEPENDENT COURSEWORK (Please refer to the Certifications section) • 6-course Python for Everybody specialisation • SQL for Data Science • Introduction to Cloud Computing • Machine Learning • Neural Networks and Deep Learning • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization • Structuring Machine Learning Projects --------------------------------- DEGREED QUALIFICATIONS • MSc in Financial Engineering • Chartered Financial Analyst (passed 2 levels) • MSc in Management (Finance) • BA (Hons) Economics

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  • Statistics
  • Quantitative Finance
  • Chartered Financial Analyst
  • Real Estate Financial Modeling
  • Forecasting
  • Trading Automation