Oct 2024 – Sep 2025 · 11 mos · Mumbai, Maharashtra, India · Remote

• At QuantInsti, I contributed to the Quant Content & Research division, working closely with the Marketing team to design, develop, and validate educational and research material focused on quantitative finance and trading.
• My role combined technical depth with creative communication — researching, implementing, and writing content that demonstrated the integration of AI and machine learning into portfolio and trading frameworks.
• Key Contributions:
• Developed and authored course material for “Quant for Portfolio Managers” and “Database Management for Trading”, translating complex quant and data concepts into practical, learner-friendly frameworks.
• Mentored EPAT participants on research projects, guiding them through methodology design, model debugging, and validation.
• Collaborated with the marketing and content teams as a subject matter expert to review and enhance quant-related publications, ensuring technical accuracy and conceptual integrity.
• Pioneered the use of AI in quant research content, contributing fresh perspectives to the intersection of data science and systematic investing.
• Actively engaged with learners and internal teams to foster a collaborative, research-driven culture.

Oct 2023 – Aug 2024 · 10 mos · Pune, Maharashtra, India · On-site

• Responsibilities:
• Develop and implement advanced quantitative tools, including stock selection models,
• risk and return models, optimizers, and portfolio analysis. Apply statistical methods and
• innovative thinking to enhance existing quantitative factors and integrate new alpha and
• risk modeling techniques. Conduct in-depth research, tests academic and broker research
• to inform investment strategies.
• Collaborate with portfolio management teams to translate quantitative insights into
• actionable recommendations. Work closely with internal Quantitative Research Analysts,
• fostering information sharing and expertise leverage. Manage daily production cycles and
• handles all aspects of data management, including IT support.

Apr 2023 – Oct 2023 · 6 mos · Mumbai, Maharashtra, India · On-site

• Responsibilities:
• The Data Scientist role involves supporting quantitative research and risk management using data science and machine learning techniques. Key responsibilities include coding, building and backtesting equity market models, creating stock selection and portfolio frameworks, developing asset allocation models, and analyzing financial and statistical data. Additional tasks encompass data collection, processing, ad-hoc analysis, database management, research synthesis, report writing, and maintaining strong communication and interpersonal skills.
• Contributions:
• The primary responsibility I undertook was to revamp the existing system in order to enhance its efficiency and automate work processes. I have successfully automated Bloomberg-related tasks using Python, and built centralised database that
• facilitated the integration of additional workflows.
• To enhance investment efficiency, I designed and implemented a Quant-AI portfolio engine that could generate customized portfolios based on specific requirements. This tool simplified portfolio creation and management, offering tailored solutions for different investment needs.
• I contributed to the development of detailed portfolio reports enabling stakeholders to gain deeper insights into performance and investment strategies. These reports were instrumental in enhancing communication and decision-making processes.

Feb 2023 – Mar 2023 · 1 mo · Remote · Remote

• Independent Consultant providing Financial Data Science Services. Developing and Backtesting Algorithms for US market.

Sep 2022 – Jan 2023 · 4 mos · Frankfurt, Hesse, Germany · On-site

• Responsibilities:
• Drive Product development initiatives. Develop new index concepts from
• design to backtesting and implementation, Setup and Parametrize new
• indexes, Development, Maintenance and Quality checks. Handling Client's
• Requirements, Deliveries and Proposing new solutions.
• Tools : Python and Excel.
• Contributions:
• Developing Capping Algorithms: This project aimed to develop an algorithmically driven weight distribution engine for capping indices that complies with indexing regulations instead of relying on constraintbased optimization techniques. The engine was optimized to proceed linearly and produced acceptable solutions without iterating through weights. It had the ability to perform pre-checks and determine the possibility of conduct. The Group Capping was a challenging task that was solved with an optimized approach that arrived at a solution in a single cycle. The Floor capping was developed with internal adaptability to shift to different levels, making it easier to automate and create a new kind of adaptive indexing methodology.
• Automating Reports: The Capping process needed to be done for every rebalancing period before putting in back-test engine, this iterative work was automated by using Python and Excel interaction libraries to generate output in acceptable format. This converted 12 hours of work into 15 minutes. The standardised approach also helped to detect issues which created quality check layer saving other hours spent on finding the problem.
• Crypto Index Engine: An Index Engine was developed from scratch to serve cryptocurrency index products. The process involved system design, data handling and cleaning, building index mechanics like filtering rules, buffer logic, and calculation. The engine was automated to accept a logbook with instructions such as sector, market coverage, etc. and generate indexes with Excel reporting output. This was a big and challenging project that provided a lot of learning opportunities.

May 2020 – Oct 2021 · 1 yr 5 mos · Switzerland

• Responsibilities:
• Developing, Back Testing and Converting trading strategies based on Machine Learning, Statistical & Technical indicator. Building R&D tools and supporting IT infrastructure. Delivering Analysis and Reports, Adopting Automation.
• Contribution:
• Backtest Framework and Strategy Creation: The objective of this task was to develop a scaled backtesting framework in R to automate the testing of strategies from Multi-chart and filter the equity curves of these strategies to generate an investment basket.
• Portfolio Optimization: Developing scripts for Portfolio Optimization techniques in R to select basket of strategies to yield higher sharpe.
• XGBoost Direction Signals: The project involved testing the use of XGBoost algorithm on a dataset with features engineered from candlestick patterns to generate trading signals. The feature importance matrix generated by XGBoost also provided valuable insights into strategy building.

Apr 2018 – Sep 2018 · 5 mos · Bengaluru, Karnataka, India

May 2014 – Jun 2014 · 1 mo · Pune/Pimpri-Chinchwad Area

• Basics of Fundamental and Technical Analysis of Market.