Ushas Thej Chenumalla

Trainee @ D+A Strategies

About

Graduate Physicist pursuing an MSc at King’s College London with a strong focus on quantitative modelling, derivatives research, and financial data analysis. I have hands-on experience developing and backtesting systematic trading strategies, extracting risk-neutral densities, analysing volatility surfaces, and applying machine learning to financial time series. My retail derivatives trading experience, where I designed Python-based backtesting systems and applied disciplined risk management, which has strengthened both my market intuition and analytical rigour.Beyond markets, I have led and executed technical research projects in quantum algorithms, opto-mechanics, and computational physics, giving me a strong foundation in mathematical modelling, simulation, and scientific programming (Python, C++, VBA). I thrive in fast-paced, high-stakes environments and enjoy solving complex problems using data-driven methods. I am currently seeking opportunities in quantitative research, trading, and investment roles where I can apply my analytical skill set, technical discipline, and passion for markets to contribute to impactful decision-making.

Country

United Kingdom

City

London

Industry

Higher Education

Skill

Quantitative Finance, Machine Learning, Options Strategies, Long Short-term Memory (LSTM), SciPy, Scikit, Stochastic Optimization, Algorithmic Trading, Stochastic Control, Macroeconomics, Leadership, Team Coordination, Photonics, Nanotechnology, Bloomberg Terminal, Derivatives Trading, Quantum Computing, Statistical Data Analysis, Quantitative Analysis (Finance), Python (Programming Language)

Experience

Trainee

D+A Strategies

2026-1 - Present · 9 mos

Leicester, England, United Kingdom

Supporting quantitative research and strategy analysis within a professional trading and research environment. Developing practical experience in quantitative reasoning, statistics, and analytical problem-solving

Student Representative – PGT Photonics and Nanotechnology (SSLC Representative)

King's College London

2024-9 - 2025-12 · 1 yr 4 mos

Greater London

Representing the interests of postgraduate students in the Photonics and Nanotechnology program, acting as a bridge between students and faculty. Advocating for student concerns, providing feedback on curriculum and research opportunities, and ensuring an optimal learning experience. Collaborating with faculty members and the Student-Staff Liaison Committee (SSLC) to improve academic policies and student engagement. Gathering student feedback, and proposing solutions to enhance academic and extracurricular experiences.

Retail Trader

2023-5 - 2024-8 · 1 yr 4 mos

Hyderabad, Telangana, India

As a retail trader, I oversaw money totalling one lakh and fifty thousand rupees and made profits of more than 50% on capital. Gained technical knowledge in the Indian financial market by trading long-term equities as well as index futures and options.

Fury member

Ragam National Institute of Technology Calicut

2022-3 - 2024-5 · 2 yrs 3 mos

Member of Nitc fury team.

Research Intern

Indian Institute of Technology Hyderabad

2023-6 - 2023-7 · 2 mos

Hyderabad, Telangana, India

• Conducted in-depth literature reviews and synthesized complex scientific information to facilitate knowledge sharing. • Experience with MOKE and XRD Data analysis on various CFB(Co40Fe40B20) Thin films, which were created using direct current magnetron sputtering technique and were annealed at various temperatures. • Gained knowledge of quantum and spintronics devices, as well as a good understanding of the working principles of spin-polarized circuits and magnetic tunnel junctions. Analysed data to produce written reports and visual representations.

Education

QuantInsti

Finance and Financial Management Services

2026-2 - 2026-9 · 8 mos

Professional certification (120+ hours, proctored exam): Python-based quantitative strategy development and backtesting; market microstructure and execution algorithms; derivatives (options, futures, FX); statistical arbitrage, time-series modelling (ARIMA, GARCH), and Monte Carlo simulation; machine learning for trading (PCA, tree-based models, reinforcement learning); portfolio optimisation, risk management, and live/paper trading using broker APIs.