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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.

I help organizations transform complex data into actionable insights that drive growth, improve decision making, and create measurable business value. With a PhD in econometrics, statistics, and causal inference and more than a decade of quantitative experience, I specialize in data science, predictive modeling, survey research, marketing analytics, business intelligence, experimentation, and advanced statistical analysis. My experience spans e-commerce, iGaming, insurance, banking, non-profit, automotive, telecom, retail, healthcare, engineering, and research sectors. I have delivered projects ranging from forecasting, predictive modeling, customer analytics, and large-scale survey research to marketing measurement, attribution, and end-to-end analytics platforms. I have extensive experience working with survey and observational data, including questionnaire design support, survey data cleaning and validation, scale and index construction, missing data handling, exploratory analysis, statistical modeling, and results visualization. I have analyzed customer, employee, stakeholder, healthcare, and research survey data using techniques such as logistic regression, count models, multilevel models, Bayesian methods, and causal inference approaches, translating complex findings into actionable recommendations. I have extensive experience building data-driven solutions using Python and SQL, including data analysis, machine learning, forecasting, statistical modeling, Bayesian analysis, survey analytics, customer segmentation, and decision-support systems. My expertise includes Marketing Mix Modeling (MMM), Multi-Touch Attribution (MTA), incrementality measurement, A/B testing, geo experiments, brand lift studies, causal inference, and Bayesian methods. Experienced in developing Bayesian MMMs using PyMC-Marketing and Google Meridian, I help organizations understand the true drivers of business performance by quantifying channel contribution, adstock and saturation effects, ROI, and budget optimization opportunities. I translate complex analytical findings into practical recommendations that stakeholders can confidently act upon. I have worked extensively with data from Google Ads, Meta, TikTok, LinkedIn, CRM systems, call-tracking platforms, survey platforms, web analytics tools, customer databases, and cloud data warehouses. My technical background includes HubSpot, Datacor, BigQuery, GA4, Snowflake, and custom internal data sources, with a strong focus on data integration, validation, ETL processes, and scalable analytics workflows. Beyond analysis and modeling, I develop dashboards, visualizations, and decision-support applications using Streamlit, Looker, Plotly, and business intelligence platforms. I am passionate about making data accessible and useful, whether through executive dashboards, interactive analytics applications, or clear communication of complex statistical results. I also have extensive experience teaching, mentoring, and collaborating with technical and non-technical stakeholders. As a former university professor, I designed and taught graduate-level courses in statistics, econometrics, quantitative methods, and research design. Whether supporting business leaders, training analysts, or working with cross-functional teams, I focus on delivering solutions that combine analytical rigor with practical business impact. Whether the goal is building predictive models, improving reporting infrastructure, analyzing survey data, conducting statistical research, optimizing marketing investments, measuring incrementality, forecasting business outcomes, or developing advanced analytical solutions, I bring a rigorous quantitative approach and a proven track record of turning data into decisions.

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

🏆 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

Top Rated Economist | Data Analyst | Statistician | Quantitative & Qualitative Researcher | Survey Analyst | Statistician Econometrician | Research Analyst | Survey Specialist | Visualization Expert ✅ Lead Analyst @Suncor Energy. Previously, a Business Analyst @NNPC & @Pan Allen Energy ✅ A Consulting Researcher @Statisda with high level research experience -000's of offers closed ✅ Able to Gather and transform data into publication-ready insight for Businesses & Researchers ✅ Econometrics| Time-series |Panel data |Cross-sectional |Non-parametric |ANOVA | Regression ✅ Adapt to any writing & formatting style; MLA, Harvard, APA, Chicago, IEEE ✅ Tools: SPSS | Stata | EViews |R | Python | Power BI | Tableau | Excel ✅ 100% original, Copyscape-verified work with unlimited revisions ✅ Professional dashboards, reports, visualizations & clear storytelling My Services: Thesis, Dissertation & Research Editing, Proofreading & Guide ⭐ Areas of focus; social sciences; Business, Finance, Economics etc ⭐ Data Collection, Literature reviews, case studies, whitepaper guide ⭐ Assistance on In-depth literature review using peer-reviewed journals ⭐ Guide to Perfect formatting in APA, MLA, Chicago, Harvard or IEEE ⭐ Insight on SEO-optimized where needed, while keeping full scholarly rigor ⭐ Professional structure with tables, figures & references Time Series Analysis ⭐ Unit root testing/ Stationarity ⭐ Cointegration analysis ⭐ Long Run Regression ⭐ Auto-Regressive Distributive Lag ⭐ Error Correction Model ⭐ Vector Error Correction Model ⭐ Simultaneous Equation ⭐ Vector Auto-Regressive model ⭐ Generalized Method of Moment ⭐ 2 Stage Least Squares ⭐ Volatility Analysis ⭐ Instrumental Variables ⭐ GARCH Modelling ⭐ ARCH Modelling ⭐ Variance Decomposition ⭐ Impulse Response Model ⭐ Volatility Check ⭐ Auto Correlation ⭐ Walds Test ⭐ And a lot more Panel Analysis ⭐ Panel Unit Root ⭐ Panel Cointegration ⭐ Fixed Effects OLS ⭐ Pool Ordinary Least Square ⭐ Random Effects ⭐ Random Walk ⭐ Fully Modified OLS ⭐ Canonical Regression ⭐ Graphical Presentation ⭐ Online Consultation ⭐ And a lot more Cross Sectional Analysis ⭐ Binary Models: Tobit ⭐ Probit Model ⭐ Logit Model ⭐ Multinomial Analysis ⭐ Factorial Analysis ⭐ Linear Regression ⭐ Correlation ⭐ Delphi Method of Estimation ⭐ Multivariate analyses ⭐ Simultaneous equations ⭐ And a lot more Non Parametric Analysis ⭐ Kruskal-walis Test ⭐ Mann-Whitney Test ⭐ Friedman Test ⭐ Chi-square ⭐ Kappa Measure of Agreement ⭐ Wilcoxon Signed-rank Test ⭐ And a lot more Group Comparison ⭐ T-Tests ⭐ Effect Size ⭐ One Way ANOVA ⭐ Two Way ANOVA ⭐ Multivariate Analysis of Variance ⭐ Analysis of Covariance ⭐ Generalized Linear Models ⭐ And a lot more Survey & Questionnaire ⭐ Questionnaire Design ⭐ Survey Design ⭐ Survey Analysis ⭐ And a lot more Preliminary & Descriptive Analysis ⭐ Massaging and Cleaning ⭐ Data Visualization Graphs & Tables ⭐ Development of Scale ⭐ Cronbach’s Alpha Coefficient ⭐ Mean, Frequency ⭐ Proportions, Charts, Graphs ⭐ Assessing Normality ⭐ Trend Analysis ⭐ Sampling Distributions ⭐ PowerPoint Presentations for Statistical Results ⭐ And a lot more Interactive Dashboard & Visualization ⭐ Visualize sales performance, ⭐ Performance tracking ⭐ Track project progress ⭐ Analyze market trend ⭐ Powerful insights. Tools ⭐ Microsoft Office Programs ⭐ Proficient with Python Programming language ⭐ Online survey distribution programs; Qualtrics, Survey Monkey, Google Survey ⭐ Econometrics; SPSS, STATA, EVIEWS, MAT-LAB, TABLEAU, RStudio etc

I'm a Top Rated Data Analyst on Upwork with over five years of hands-on experience in econometrics, biostatistics, statistical analysis, and forecasting. I've worked with PhD researchers scrambling before their defense, policy teams who needed econometric data analysis that could survive a room full of critics, and healthcare professionals whose clinical data analysis had to meet journal standards before submission. In every case, the goal was the same: to get the data analysis right, explain it clearly, and make sure it holds up. ⤷ Top-rated Data Analyst on Upwork ⤷ 46+ completed data analysis projects ⤷ 100% Job Success Score ⤷ Certified: Data Visualization with R (IBM) | R for Data Science (IBM) | Google Analytics Here's what I've learned after five years of data analysis work: Most projects don't fail because of bad data. They fail because the wrong question was asked, the wrong model was chosen, or the assumptions were never checked. That's the gap I close. Before I run a single test, I make sure I understand your research question, your dataset, and what your results actually need to prove. What I do for you I handle the full data analysis process — from messy raw data to clean, documented, publication-ready results. Depending on your project, that means: As your Data Analyst for academic and dissertation work, I support Chapter 3 from start to finish — research design, data preparation, model selection, assumption testing, results interpretation, and write-up in language your committee will accept. As your Data Analyst for econometric and policy research, I build models for program evaluation, policy impact, and causal inference — OLS, IV, difference-in-differences, panel data analysis with fixed and random effects, and time series data analysis with ARIMA, VAR, and cointegration testing. As your Data Analyst for biostatistics and healthcare research, I deliver clinical data analysis to publication standard — survival analysis, Cox regression, Kaplan-Meier curves, longitudinal data analysis, and multilevel modeling for health datasets. As your Data Analyst for business and financial projects, I provide data analysis that supports real decisions — predictive modeling, financial forecasting, regression analysis, and business intelligence reporting that goes beyond surface-level dashboards. Every data analysis engagement is delivered with reproducible, well-documented code in R, Stata, Python, or SPSS — so your results can be verified, defended, and built upon. Data Analysis Tools: R | Stata | Python | SPSS | SAS | EViews | Excel Whom I work with I work best with people who take their data analysis seriously. That includes PhD and Master's students whose dissertation data analysis needs to withstand a defense, economists and policy analysts who need econometric data analysis that holds up under scrutiny, healthcare and clinical researchers whose data analysis has to meet peer-review standards, and businesses or financial teams who need data analysis that actually informs decisions. If you're not sure whether your project fits — ask. I'll tell you honestly. What makes my data analysis different I don't finish a data analysis and go quiet. I tell you what the results mean, what they don't mean, and what someone might challenge — before they do. If an assumption is violated, I flag it and fix it. If a simpler method gives you a more defensible answer, I'll recommend it even if it makes my work look less complex. Every data analysis I deliver is fully reproducible, clearly documented, written in plain language, and backed by the methodology that actually fits your data — not the one that looks most impressive. Projects I handle regularly Dissertation data analysis — Chapter 3 through final results Panel data analysis with fixed and random effects Time series data analysis and ARIMA forecasting Survival data analysis for clinical and public health research Econometric data analysis for policy evaluation and causal inference Business data analysis for forecasting and financial modeling Healthcare data analysis for medical research and clinical trials Survey and cross-sectional data analysis for academic and applied research Have a dataset, a deadline, or a research question you're not sure how to frame? Send me a message. I respond within an hour, and I'll tell you straight away how your data analysis should be approached and whether I'm the right person to help.