Senior Quantitative Researcher

ANJALI THAWAIT

+44-759*-****** +91-963******* ***********@*****.***

ABOUT MYSELF

Senior Quantitative Researcher with 3+ years of experience in statistical modeling, time series analysis, and alpha factor generation. Specialising in the application of data science to develop robust pricing models and trading signals. Skilled of refining portfolios through systematic strategies and implementing effective investment and trading solutions. ACADEMIC QUALIFICATION

Judge Business School, University of Cambridge, UK Expected Graduation - June 2025 Master of Finance (MFin)

Indian Institute of Technology Bhilai, India 2018 - 2020 Master of Technology (MTech), Electrical Engineering Honours: Senate Award for Academic Excellence

Chhattisgarh Swami Vivekanand Technical University, India 2012 - 2016 Bachelor of Engineering (B.E.), Electronics & Telecommunication Engineering WORK EXPERIENCE

State Street Global Advisors, Bengaluru, India September 2021 - April 2024 Senior Quantitative Researcher - Team Lead

• Conducted in-depth research on emerging markets to identify alpha factors across diverse regions and sectors, while building a solid understanding of capital market financial instruments

• Proactively engaged with portfolio managers in routine portfolio analysis discussions to refine strategies and rebalance assets as necessary, responding to changes in correlations and improving overall performance

• Partnered with cross-functional teams to implement a comprehensive resource management plan, regularly evaluating client needs and project goals to enhance workflow efficiency and project delivery

• Designed a language processing approach for file classification and text detection, generating alpha factors that boosted efficiency and contributed to achieving $80 billion in AUM on the Databricks platform using PySpark on Linux

• Enhanced equity asset management strategies by integrating diverse ESG factors, resulting in approximately $40 million in AUM, while lowering transaction costs by 2% through improved execution strategies

• Performed sentiment analysis on news data to identify potential stocks and developed investment strategies, achieving a notable excess alpha of 5% over the S&P 500

Acsys Investments Private Limited, Chennai, India March 2021 - August 2021 Quantitative Research Analyst

• Applied mathematical and statistical modelling along with certain machine learning models to evaluate asset risk and re- ward, and implemented a backtest model for efficient allocation, managing around $10 million in AUM. Leveraged technical analysis to assess security pricing and trading volume trends, crafted strategies using frequency analysis, and achieved a significant Sharpe ratio of over 2.5

• Utilised optimisation techniques, such as Monte Carlo simulations, to refine ML model parameters and build regression and predictive models to enhance predictability and achieve an 18% annual return through comparative model analysis

• Programmed and optimised market indicators (RSI, Stochastic, MACD, Volume Profiles) to enhance model accuracy, util- ising data modeling, exploration, and visualisation techniques in Python, R and C++

• Maximised client portfolio returns by making strategic trading decisions, managing risk with optimised drawdowns, and collaborating with third-party vendors for trading signals

• Managed project efforts to implement and optimize advanced data solutions, focusing on model development, documenta- tion, benchmarking, and validation in a fast-paced environment OTHER RELEVANT EXPERIENCE

Cambridge Student Investment Fund (CAMSIF), Cambridge, UK September 2024 - June 2025 Quantitative Researcher

• Developed and implemented alpha strategies for mid-frequency trading in the UK market, leveraging price and volume data analysis to achieve a Sharpe ratio of approximately 1.5, while managing a portfolio worth £25,000 Untrade, India August 2024 - February 2025

Quantitative Researcher - Freelancing

• Achieved an average monthly return of 25% on an initial $10,000 investment, with a Sharpe ratio exceeding 3 for Bitcoin and Ethereum, and scaled the codebase to support additional digital assets RESEARCH PUBLICATIONS

Comparative Analysis of Deep Learning and Machine Learning Techniques for Power System Fault-Type Classification and Location Prediction (IEMTRONICS June 2022)

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