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Dezdiar Tavadia · Mumbai, Maharashtra, India.
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About
💼 Family Office Head | 🤖 Data Scientist-Turned-📈 Investor | 🛠️ Human Swiss Army Knife
I lead the investment strategy for my family office, focusing on building diversified portfolios across public markets, private equity, funds, and alternative assets. My approach combines quantitative rigor with disciplined portfolio management — emphasizing robust modeling, risk management, and long-term capital preservation.
With a background in data science and engineering, I bring a structured, analytical lens to investing. I believe that well-designed systems, data-driven insights, and careful stress testing are critical to making informed capital allocation decisions.
Prior to managing capital, I worked on developing advanced analytics and AI solutions at organizations including the University of Cambridge, Rolls-Royce, Airbus, and Ford. My work focused on predictive modeling, decision-support systems, and large-scale data analysis - solving complex operational and strategic problems using machine learning and statistical techniques.
My academic background includes an MSc in Data Science, an MBA, and a BTech in Mechanical Engineering, which together enable me to bridge the worlds of technology, data, and finance.
Throughout my career I have built:
-> Quantitative trading and investment models for systematic portfolio management -> Data architectures and analytical frameworks for investment decision-making -> NLP systems that transformed unstructured operational data into actionable insights
-> Digital twin systems for predictive monitoring and operational optimization -> Automation solutions that improved efficiency across complex workflows
I am a hackathon winner and Lean Six Sigma Green Belt, and I strongly believe that combining rigorous data analysis with sound strategic thinking creates a durable competitive advantage — both in technology and investing.
💡 Focus: Deploying capital and technology with discipline, clarity, and measurable impact. 🚀 Open to connecting with founders and operators building high-impact, scalable businesses.
Experience
Business OwnerCurrent
DataQuant Solutions · Mumbai, Maharashtra, India
Jan 2024 – Present
> Led the digital transformation of a traditionally managed investment setup into a fully data-driven, systematic decision-making platform across equities, private equity, mutual funds, ETFs, and commodities by designing end-to-end data infrastructure, automating workflows, and embedding analytics, converting legacy practices into evidence-based strategies > Served as both strategist and execution lead, formulating tactical and strategic asset allocation, position sizing, and risk overlays while aligning quantitative insights with long-term philosophy and governance > Designed and deployed systematic stock-selection and alpha-generation models using multi-factor frameworks (growth, value, momentum, quality, volatility), machine learning, and econometric testing; conducted iterative trialing, backtesting, and stress testing to isolate sustainable alpha > Conducted quantitative evaluation of mutual funds and ETFs, benchmarking risk-adjusted returns (Sharpe, Sortino, Information Ratio), factor biases, and regime performance to optimize allocation across equity, debt, hybrid, and alternative funds > Performed advanced commodities analytics using time-series forecasting, volatility clustering (GARCH), and cross-asset correlation to identify inefficiencies, hedging opportunities, and diversification benefits > Built automated trial-and-error frameworks within Bloomberg, creating sandbox environments for hypothesis testing, scenario replay, and hybrid live-paper runs to validate strategies before scaling portfolios > Developed portfolio optimization and risk frameworks with mean-variance optimization, Black-Litterman modeling, Monte Carlo simulations, and Value-at-Risk (VaR) to balance growth with liquidity and drawdown controls > Leveraged LLMs within Bloomberg to create AI assistants for analyzing annual reports, research notes, and financial documents, enhancing research, due diligence, and compliance-friendly reporting with explainability and ethical use
Data Scientist - People and Culture
Cambridge University Press & Assessment · Cambridge, England, United Kingdom
Dec 2022 – Dec 2023
> Data Scientist in People Analytics, leveraging insights for workforce planning, talent acquisition, retention, performance management, and employee well-being > Designed & implemented data pipelines to integrate workforce data from HR systems (Workday, eArcu, Culture Amp, SharePoint, etc.), automating ingestion, cleansing, and consolidation for centralized storage and seamless accessibility across the People team > Led Power BI dashboard development for key HR metrics (headcount, internal mobility, employee sickness, recognition, new hires, attrition), creating data models with Python & advanced DAX to deliver interactive visualizations that improved workforce planning, reduced turnover, & enhanced HR efficiency > Developed a three-stage NLP framework to automate shared mailbox analysis: Stage 1 focused on topic modeling & traffic metrics; Stage 2 identified high-volume query drivers across multiple inboxes; Stage 3 applied knowledge graph techniques for deep dive sub-topic analysis, enhancing operational efficiency > Managed and automated recurring HR data extraction for external stakeholders (insurance renewals, national statistics, audits), using version-controlled codebases to ensure timely, compliant, and accurate data delivery > Delivered exploratory data analyses and ad-hoc HR reporting for gender pay gap, wellbeing index, new hire topic modeling, and global workforce visualizations, combining automated processes via GitLab and flexible Excel outputs for data-driven decision-making > Explored AWS & Azure OpenAI Studio, developing GenAI prototypes and tutorials using dummy HR data, assessing API integrations, scalability, and security to evaluate business feasibility and support AI-driven automation > Collaborated with CTO in a cross-functional initiative, advising on the design of a centralized data lake and AI adoption roadmap, focusing on GenAI, data governance, ethical AI, and aligning technical capabilities with business objectives and compliance
Education
Skills
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