Hire the Best Quantitative Finance Professionals

DATA SCIENTIST — everything I build, you can verify the math. I deliver transparent, auditable data models where every number can be checked. Specializing in quantitative research, statistical modeling, Data insights and automation. QUANTITATIVE RESEARCH & PORTFOLIO CONSTRUCTION : I build and backtest equities, with live execution and kill switches on Alpaca where needed. Position sizing, risk allocation, rebalancing logic, all of it grounded in the math, not gut feel. Quick example of how I think: a momentum strategy covering 145 stocks was delivering 41% CAGR. Expanding to 245 stocks dropped it to 36%. Counterintuitive. The root cause: New stocks were selected on hot recent momentum, so by entry their cycle was already done. Built a 5-filter Monte Carlo system, tested 700+ parameter combinations, to identify which satellites are early-cycle vs peaked. Result: 43%+ CAGR with lower drawdown than the 145-only baseline. The problem wasn't the strategy. It was the timing of entry. Another example: I built a live MBS (Mortgage-Backed Securities) forecasting and trading system for a fund client, covering 265+ cohort securities across weekly, monthly, and quarterly return horizons. The interesting part wasn't the ML, it was the feature design. Standard technical indicators on bond prices are noise. What actually drives MBS price movements is duration, convexity, prepayment rates, regime variables like curve steepness, VIX, and Fed MBS holdings. Every feature built from first principles, nothing borrowed from a vendor that can't be verified. This is where I spend most of my time. Systematic backtests, portfolio construction, Monte Carlo optimization. No black boxes, no ML hype every output can be checked in a spreadsheet. Not "add more data." Find the actual structure driving returns, and build features that reflect it. DATA SCIENCE & ANALYTICS I've worked across real estate, e-commerce, and financial analytics, usually called in when a team has data but no clear answer yet. I focus on getting to a decision you can act on, not a 40-slide deck. Results worth mentioning: ML automation cut a client's data processing from 286 days to 1 day, after they'd run the same manual process for two years without realizing it was automatable Ad spend attribution model that improved campaign ROI by 25% WHAT MAKES ME DIFFERENT ▸ I show my work. Every model, every analysis, every number comes with the method behind it, so you're never trusting a black box ▸ I'd rather tell you a strategy doesn't hold up than build you something that looks good and fails in real world. ▸ I scope real problems fast: send me what you're working with and I'll tell you within 24 hours if and how I can help

I am an experienced quantitative financial researcher with 15+ years of experience in the field. Before that, I used to be a Physicist in academia, where I had a passion for cosmology (my PhD thesis was in this) and Quantum Physics (I currently run a blog on Linkedin on this). As part of this background, I can contribute to gigs that involve any of the following, but not limited to it: • Data analysis (optimization, clustering, especially dealing with very noisy data, applying any mathematical model on data) • Machine Learning (random forests, adversarial networks, recurrent networks, ....) • Proficient in C, Python, C#, but you can definitely catch my interest if you have a problem involving the Wolfram Language • Passionate about Quantum Physics, so I would definitely enjoy projects that require working in theoretical Quantum Physics. • I can do projects that involve Mathematics (Chaos Theory, Dynamical Systems, Real Analysis). • Last, but not least, I have experience in the full cycle of generating systematic strategies from prototyping to implementing into production all the way to monitoring the execution.

I help research teams and technical founders build and validate reliable, testable scientific and quantitative systems — research prototypes, backtesting frameworks, production execution systems, simulation engines, and performance-critical pipelines. I also conduct independent technical review of quantitative models and research frameworks. If you're dealing with slow code, messy data, a model you can't trust, or a research prototype that needs to become production, I can turn it into a robust, reproducible result. Background: PhD Physics (NYU, 2018). Quantitative Research at JPMorgan Chase — derivatives pricing on a 1M+ LOC C++ library. Max Planck Institute postdoc — general-relativistic hydrodynamics on 1,000+ core HPC clusters. 13 peer-reviewed publications, 1,300+ citations, h-index 12. WHAT I DELIVER ▸ Quant & Options Engineering Backtesting frameworks (event-driven or vectorized), walk-forward, leakage checks Options analytics: Greeks, IV surfaces, Black–Scholes and numerical methods Research → production pipelines (clean architecture, tests, logging, monitoring) Execution integrations (e.g., IBKR) and robust order / risk handling Market data ingestion, cleaning, corporate actions, quality control ML and statistical time-series models with proper cross-validation (no leakage) ▸ Scientific Computing & Research Tooling For physics, chemistry, biology, engineering, and any domain where the core problem is mathematical or computational. Custom numerical solvers (finite volume / finite difference, spectral, particle methods) with stability and convergence analysis Optimization engines (Bayesian, gradient-based, evolutionary) for experimental design, formulation, and parameter search Simulation frameworks from prototype to production grade Scientific data pipelines: ingestion, transformation, quality control, reproducible workflows Verification and validation: benchmarks, unit / regression tests, convergence studies Analysis tools, dashboards, and reporting infrastructure for research workflows Air-gapped and reproducible deployments where IP sensitivity or regulatory context requires it ▸ Quantitative & Mathematical Review (NDA-protected) Independent technical review of quantitative models, frameworks, and research Verification of internal consistency, identifiability, hidden assumptions, and mathematical correctness Assessment of whether the formal structure supports the conclusions drawn from it Implementation review against specification: numerical stability, edge cases, code-to-spec fidelity ▸ HPC & Performance Engineering Distributed computing (MPI / OpenMP / CUDA), GPU optimization, memory and I/O tuning Inference and training infrastructure at scale Profiling, refactors, and speedups for codebases that need to run reliably under production load WHY CLIENTS WORK WITH ME - Trustworthy work — research prototypes turned into tested, reproducible production code; models reviewed against their own claims - De-risking — failure modes surfaced early (leakage, overfitting, edge cases, scaling bottlenecks) - Maintainability — clean architecture, docs, handover-ready delivery your team can extend - Communication — clear milestones, concise updates, realistic timelines, no surprises - Math ↔ engineering bridge — strong intuition for both theory and implementation IDEAL PROJECTS - Quant strategy development, backtesting, and research infrastructure - Options analytics and derivatives tooling - Mathematical review of quantitative manuscripts, white papers, or research frameworks - Independent validation of production models against specification - Market data pipelines and reproducibility upgrades - Performance optimization of slow Python / C++ codebases - Distributed training, GPU optimization, and inference serving for ML workloads - Custom scientific or industrial simulation and numerical software - Internal R&D tooling for research labs and technical teams If this sounds like a fit, message me with a brief on your current setup and success criteria — I'll let you know how I can help.

⭐ Top 1% of Data Science talent on Upwork ⭐Trusted by 50+ clients worldwide⭐ 120+ projects and 7+ years of experience⭐Clear communication, transparent pricing, and top quality. I have worked across different sectors, including: 1.1 InnoSight Financial Planning (USA): I designed an optimal portfolio based on back-projected AI-powered financial indices. 1.2 Aeuthux (USA): I led a team of data scientists to build a web platform supporting investment decision-making. 1.3 Placeholder LLC (USA): I built a trading bot using machine learning deployed on AWS to operate in cryptocurrency markets. 1.4 ARCA-X (QATAR) I wrote research papers on the financial structure of non-central banking systems.

I reply to all direct messages, even if you just want to connect. I'm a quantitative researcher and developer with a background in Theoretical Physics (PhD, high-impact publications) and 6+ years of professional experience in algorithmic trading. I've built my own profitable crypto strategies and have a track record of helping clients — from wealthy individuals to funds — hit their goals. I'll help you measure your edge and turn it into a robust, automated system. Lately I've been going deep into prediction markets. I build data pipelines and ML-driven trading systems for Polymarket and Kalshi, working directly with order book data — bid/ask dynamics, liquidity depth, spread behavior, imbalance signals — to extract edges that most participants simply don't see. If you're operating in this space, I know the infrastructure and the market microstructure well. This is how I solve your problems: Validate Your Edge: Before a single line of production code is written, I apply a rigorous scientific approach to backtest and optimize your strategy, stress-testing it for statistical significance and robustness. Build High-Performance Bots: Clean, well-documented Python code for bots on centralized exchanges, DEXs, and prediction markets. Reliability is key. Go Deep on Order Books: I build systems that collect, store, and analyze full order book snapshots in real time — and turn that data into actionable signals. Develop Custom Trading Tools: Proprietary indicators and signals designed to capture the specific inefficiencies you're targeting. Enhance Existing Systems: I help clients refine and improve the performance of their current algorithms. My clients keep coming back because I treat their problems like my own and don't cut corners. If you're ready to move from concept to a fully operational, edge-driven system — whether that's crypto, prediction markets, or both — I'm the partner you're looking for.

Financial markets analyst and trading educator with experience across options markets, volatility analysis, live market instruction, and financial media content. I specialize in translating complex market behavior into clear, actionable insights for retail traders, fintech platforms, and financial audiences. My background combines real time market analysis, derivatives education, client engagement, and financial writing across fast moving market environments. Alongside over 2,400 hours of live market instruction and trader development, I also contribute written market commentary and analysis focused on equities, options activity, macro themes, sentiment shifts, and volatility behavior. My experience includes: • Options market analysis and derivatives education • Volatility and market structure interpretation • Financial content creation and market commentary • Live chart analysis and market walkthroughs • TradingView and technical analysis workflows • Risk management and position sizing education • Client onboarding and trader support environments • Live virtual instruction and interactive learning sessions I work with: • Financial media and content platforms • Financial audiences seeking structured market education • Educational and research focused market teams • Traders seeking structured market understanding • Businesses needing market commentary or financial content support My approach prioritizes clarity, structured thinking, risk awareness, and practical communication over prediction driven content or hype. Disclaimer: I provide financial education and market commentary only and do not provide personalized investment advice.