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I’m a quantitative-minded data analyst with a strong background in finance, statistics, and programming, currently pursuing an M.S. in Mathematical Finance & Financial Technology at Boston University. I help clients turn messy data into clear, defensible analysis, whether that’s financial modeling, regression analysis, time-series work, or backtesting trading ideas. My focus is on correctness, transparency, and results you can actually explain. I can help with: Data cleaning and exploratory analysis (Python, pandas, R) Regression & statistical modeling (cross-sectional, time series, panel data) Financial and portfolio analysis (risk metrics, factor models, performance) Backtesting trading or investment strategies Research support for academic or professional projects My background includes work on financial indices, crypto and blockchain research, and quantitative modeling projects, and I’m comfortable working independently with minimal supervision. I care a lot about getting the details right-assumptions, edge cases, and interpretation, not just producing outputs. If you need a reliable analyst for part-time or project-based work who can both code and explain results clearly, I’m happy to help.

I’m a quantitative researcher & trader 📈 with nearly 10 years prior experience at a major Chicago market making and high frequency trading firm. I can help your team analyze and extract insight from large datasets, design and evaluate systematic strategies, build and validate backtests, and troubleshoot complex execution, latency, & performance issues. If you need help with ✅ backtesting ✅ strategy evaluation ✅ execution analysis ✅ reconciling backtest vs. live performance ✅ debugging/troubleshooting trading strategies ✅ latency investigation and performance benchmarking ✅ data visualization ✅ Conveying complex analytical concepts into long form content or reports ✅ Ghostwriting advanced finance and trading content that performs on social ✅ Growing your X or LinkedIn account with organic content in the finance & trading niche ✅ Or simply extracting meaningful and insightful conclusions from very large datasets, I can bring institutional grade expertise to your project. 🌟 Additional highlights • Extensive experience with ₿ crypto market data and the Defi and NFT ecosystems • Series 7 license during prior tenure • LIVE trading floor experience and responsibility for design, refinement, support and monitoring of a ✅ 50+ server global trading strategy infrastructure executing ✅ 100M+ shares per month. • On our peak days, we approached 20% of the SPY market volume in addition to the rest of the portfolio. • Market microstructure and orderbook expert working directly w/exchange market data. • Regularly reported strategy performance, infrastructure behavior, and operational findings to fellow traders and management, including a one time direct report of critical findings to CME Group. • Strong general knowledge of multiple assets classes and global financial markets and news cycles 📊 Strategy Research & Backtesting • Conducted terabyte scale tick data research including microsecond level replay, queue position and slippage modeling, fill probability and order book interaction studies, & latency sensitivity analysis • Maintained and refined predictive models across multiple live product classes, exchanges, and market regimes that traded over 100 million shares per month. • Monitored live performance for microsecond sensitive strategies, diagnosing discrepancies between simulated vs. live behavior providing insight into signal degradation, slippage, order placement logic, parameter sensitivity, and venue specific execution problems. • Deep experience in predictive modeling including time series forecasting, robustness testing, and systematic strategy evaluation. • Collaborated with fellow traders, researchers, and developers to refine models based on execution behavior across markets. 👷 Infrastructure & Systems Experience • Supported a LIVE complex low latency global trading infrastructure across Chicago, New York, Paris, Frankfurt, and London, daily for nearly 10 years. • Deep knowledge of internal networking infrastructure at major exchange colocation facilities worldwide, and its effect on latency sensitive strategies. • Understanding of idiosyncratic matching engine and order book mechanics at major worldwide equity and futures exchanges • Hands on experience with ITCH, OUCH, FIX, CME MDP 3.0 exchange data protocols • Direct exchange connectivity and feed handler experience • Packet and message level execution & backtesting experience • Low latency server coordination and monitoring • Contributed to the rollout of a new generation of low latency trading software, including execution research performance validation, .deployment & production monitoring • Operated in CI/CD environments with Git, Atlassian tools and an Agile iterative model deployment workflow coordinated with a large in house development team. 🌎 Cross-Asset & Cross-Region Markets Experience across major US equity and ETF markets and European index futures including • ES, YM, NQ, TF US Futures • FESX, FDAX, CAC (Euronext Paris), FTSE (LIFFE/ICE Europe) European Futures • Major US ETFs • Major US large cap Equities • Exchange venues: CME, NASDAQ, NYSE, ARCA, BZX/BYX, EDGA/EDGX, IEX, Eurex, LIFFE, ICE, Euronext Paris, and dark pools. • Options competency but not in direct trading capacity 📈 Strategy development types • futures vs. equities arbitrage • event driven stat arbs • short term predictive models • fill and hedge/spread driven strategies • basis strategies • volatility arbitrage • both market making and 'taker' strategies • cross venue and cross region latency sensitive strategies ⚒️ Technical Skills & Credentials • R (expert), SQL (advanced), VBA, Bash scripting • C++ environment familiarity (not a developer role) • Tradingview, Pinescript • Strong familiarity working in Linux environments • Git, CI/CD workflows, Atlassian tools (JIRA etc) • AWS • Backtesting in Hadoop framework • Hands on experience with ITCH, OUCH, FIX, CME MDP 3.0 exchange data protocols • FINRA Series 7 licensed (held during tenure)

I turn messy, high-stakes data into decisions people can act on. Over 15+ years and 66 Upwork contracts (~4.9/5 stars across rated jobs, 84% perfect 5.0s), I've built my practice on one promise: you get a clear answer, a tool your team can actually use, and plain-English communication throughout. What I do best: * Interactive R Shiny dashboards and analysis platforms: 12+ shipped, including a 20+ package bioinformatics platform and a no-code causal-inference tool for pharmaceutical analysts * Machine learning and predictive modeling: injury prediction for an NFL franchise, mortgage default risk, game simulation * Statistical analysis: hierarchical Bayesian modeling, MaxDiff and survey analytics, meta-analysis, causal inference * R package development: a production package built over a 952-hour flagship engagement, plus a released CRAN package * Quantitative finance: DCC/GARCH modeling, asset allocation, backtesting frameworks Domains I know well: healthcare and EHR data, bioinformatics and genomics, finance and trading, marketing analytics, survey research, and sports. How I work: independently, end to end. Most clients hand me a problem, not a spec. I scope it, deliver in agreed milestones, and explain the results so non-technical stakeholders can make the call. That approach is why most of my work is repeat business, and why "Committed to Quality" and "Clear Communicator" are my two most-endorsed client tags. M.S. in Bioinformatics from Johns Hopkins. Co-author on 8 peer-reviewed papers. US-based native English speaker. If you have data that should be driving a decision and isn't yet, let's talk.

I help businesses turn data into decisions that drive results. With an MS in Mathematical Finance & FinTech from Boston University and hands-on experience at a primary US Treasury dealer, I bring a rare combination of quantitative finance expertise and modern data science skills. What I can build for you: - Financial data dashboards and automated reporting - Machine learning models for prediction and classification - NLP pipelines for sentiment analysis and text analytics - RAG-powered AI tools using LangChain and OpenAI - Quantitative models for risk analysis and financial forecasting Recent projects include a real-time Financial News Sentiment Analyzer using RAG and GPT, a Bank Customer Churn prediction model, and an interactive Financial Data Dashboard, all deployed and live. I work in Python, SQL, Pandas, Scikit-learn, LangChain, Streamlit, and PySpark. I deliver clean, documented, production-ready work. If you have a data problem that needs solving, let's talk.

🏆 EXPERT-VETTED TOP 1% ON UPWORK |⭐ 5-Star Ratings | Data Analytics and Predictive Modeling|💎MASTER/PHD THESIS Help |✅Statistics, Panel Data, Time series Econometrics |✅ R, PYTHON, SAS, Tableau, Power BI | ✅Data Science, Machine Learning Models | ✅STATA Master, Large Survey Data and Regression Analysis, Economic Policy Evaluation, Predictive Financial Modeling | 🏆PhD and 20 years of experience. ABOUT ME: 🏆 EXPERT-VETTED and TOP RATED PLUS status (TOP 1% on UPWORK) 🥇20 years of working experience with large corporations and institutions (Amazon, the World Bank, International Financial Corporation, University of Pennsylvania) 🎓PhD/Professor in Econometrics/Statistics/Data Science 💎Provide effective help for MASTER/PHD THESIS With proven tracked records of success in Upwork (Expert Vetted Top 1% and Top Rated Plus), overwhelmingly positive feedback with 5* ratings, my PhD and 20 years of experience, I can definitely help you bring out the highest quality, great results for your project and achieve your business goals. I have over 20 years of experience in empirical research, statistical analysis, econometric modeling, data science, machine learning models, data analysis (panel data analysis and time series analysis, complex survey design and analysis), data visualization, storytelling, and project management. I enjoy creating dashboards of data analytics to provide data insights, data trend, meaningful graphs, statistical analysis, story-telling summary that serves as key benchmark to influence business decision. Moreover, I also provide robust statistical evidence, regression/correlation analysis, machine learning predictive models to provide supportive and significant results for business projects. I also help clients understand different statistical techniques, regression techniques, learn excellent statistical language including STATA, SAS, R, and Python to make your life easier. My previous projects on machine learning and econometric modeling include the followings but not limited to: - Data Analysis, Large Survey Data Analytics and Data Visualization. - Linear regression with panel data and time series data. - Forecasting model for time series analysis - Financial modeling such as stock returns, cryptocurrency price prediction, financial indices and financial performance prediction. - Machine Learning models: Random forest, decision trees, KNN, neural network,... - Logistic models, multinomial logit models, ordered logit models. - Economics research, market research, house price, mortgage default prediction, - Program evaluation using different methods such as difference in difference, propensity score matching, instrument variables, fixed effects, regression discontinuity design. I am happy to share my knowledge on machine learning modeling as well as quantitative and analytical skills to help you succeed in your project. In addition, with my experience in participating and presenting in thousands of academic and industry conferences, supervising and guiding hundreds of college and graduate students in their thesis/dissertation, I am very confident that I can help you make a difference in your project and bring out the highest quality and great results for your Master or PhD's thesis/dissertation project. I can also provide statistical advice and statistical analysis including: - Descriptive Statistics. - Bi-variate analyses such as t-tests, correlations, chi-square tests and non-parametric tests for normal and non-normal data. - Multivariate analyses such as ANOVA, MANOVA, LDA, PCA and factoring analysis, regression analysis (see econometric models above). - Review and interpret statistical output, set up the appropriate hypothesis testing and write up conclusion and policy recommendation. - Tutoring Statistics, Math and Econometric courses: including both introduction and advanced courses. I have experience with different types of data including: - Survey data (household, market research, firm/business/country data, house price, loan performance, accounting, finance, health, labor and income, etc.) - Panel data - Time series data I help clients put together and analyze different types of survey data, panel and time series data, getting the data insights, build data trend and data stories with meaningful graphs and great visualization. With proven tracked records of success in Upwork (Expert Vetted Top 1% and Top rated), overwhelmingly positive feedback with 5* ratings, my PhD and 20 years of experience, I can definitely help you bring out the highest quality, great results for your project and achieve your business goals. Looking forward to working with you soon, Dr. Victoria

I'm a quantitative researcher/scientist who enjoys forming hypotheses, designing studies to test hypotheses, working with data, building statistical models, conducting statistical tests, and producing fully customized graphs/visualizations to show the results of quantitative research. I also enjoy creating computer simulations and building R Shiny apps. I usually do most of my work in R, though I also used Stata, Python, Excel, and SPSS in the past. I did a PhD at Stony Brook University, where I conducted quantitative research in political psychology and political economics and where I received extensive training in statistics and methods of data analysis. My coursework included basic statistics and probability, experiment design, econometrics, linear and non-linear regression models (including binomial models, multinomial models, ordinal models, count models, censored and truncated models), maximum-likelihood estimation, time-series analysis, experiment design, causal inference, instrumental variables, regression discontinuity, structural equation modeling, factor analysis, game theory, computational modeling and simulations. For several years I also taught university courses in math, statistics, and quantitative research methods for social science. I taught students how to use R, Stata, and Excel for data analysis. I taught both undergraduate students and doctoral students. I also have experience with publishing quantitative research in peer-reviewed journals, both as a solo author and as a team. In my freelance consulting work, I have advised clients on study design and statistical analysis for projects in many different fields of research, including psychology, sociology, economics, linguistics, mental health, medical treatment and recovery, business hiring practices, predictive modeling of asset prices, consumer preferences for market research, and others. - James Cragun