Hire the Best Bayesian Statistics Specialists

Hi, I’m Dr. Soleyman Paydar — a Statistician & Data Analyst with 17+ years of experience. I specialize in performing high-quality statistical analyses for academic, medical, and business research, and I have extensive experience preparing complete data analysis chapters for Master’s and PhD dissertations. My services include: • Comprehensive data analysis using SPSS, AMOS, and SmartPLS • Clear interpretation of quantitative results • Full APA-formatted reporting, including tables, figures, citations, and narrative explanations ready to insert into your thesis or manuscript • Guidance on methodology, research design, and statistical justification • Free initial consultation to discuss your research needs and determine the best statistical approach With a strong background in advanced statistical modeling, SEM, PLS-SEM, multivariate analysis, and non-parametric methods, I ensure your results are accurate, defensible, and aligned with academic standards. I’m committed to delivering professional, timely, and high-quality work. Feel free to reach out—I look forward to supporting your research success. Very Respectfully, Dr. Soleyman Paydar

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.

I am a dedicated and skilled Systematic Review, Meta-analysis, Network Meta-Analysis, Machine Learning including a predictive Model (LSTM, Regression, Logistics GB Algorithm), Time series Modelling, Panel Data Analysis, Econometrics expert with five years of enriching experience in the field of research and data analysis. My journey in the realm of systematic reviews, meta-analyses and machine learning has been marked by a fervent passion for unraveling complex scientific queries and synthesizing extensive volumes of data into meaningful insights. In my dynamic career spanning half a decade, I have delved deep into the intricate world of systematic reviews, meta-analyses and Machine learning. My expertise lies in meticulously designing and conducting comprehensive data analysis and report writing, employing rigorous methodologies to collect, assess, and interpret a plethora of research studies. My goal is to unearth patterns, trends, and discrepancies in existing research, contributing significantly to the advancement of various scientific domains. Over the years, I have spearheaded numerous research projects, where I have demonstrated my prowess in synthesizing diverse data sets. My systematic reviews have not only provided a panoramic view of existing research but have also paved the way for groundbreaking meta-analyses, amalgamating quantitative data from multiple studies to draw statistically robust conclusions. One of my significant accomplishments includes the successful completion of a meta-analysis that elucidated the efficacy of a groundbreaking medical intervention. This analysis, widely cited in academic circles, has played a pivotal role in shaping clinical guidelines and has been instrumental in guiding healthcare professionals worldwide. Methodological Proficiency: My expertise encompasses a wide array of systematic review methodologies, ranging from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to Cochrane Collaboration standards. I am adept at employing statistical tools and software, ensuring the precision and accuracy of my meta-analytical findings. Additionally, I possess a keen eye for detail, enabling me to critically appraise studies, identify biases, and offer nuanced interpretations. Contributions to the Academic Community: Beyond my individual research endeavors, I am passionate about knowledge dissemination. I have actively contributed to the academic community by publishing my findings in reputable journals, thereby facilitating the exchange of ideas and encouraging further exploration within the scientific community. I have also served as a mentor, guiding aspiring researchers in the nuances of systematic reviews and meta-analyses. Future Endeavors: As I look forward, my aim is to continue my scholarly pursuits, delving into cutting-edge research questions and collaborating with experts from diverse fields. I aspire to further refine and expand my methodological toolkit, exploring innovative approaches that push the boundaries of systematic review and meta-analysis methodologies. In essence, my journey as a Systematic Review and Meta-analysis expert is defined by a relentless pursuit of excellence, a commitment to scientific rigor, and a genuine passion for unraveling the mysteries of the research landscape. I am excited about the limitless possibilities that the future holds and am dedicated to making enduring contributions to the world of academia and scientific inquiry. Some of the research i have done include systematic review and about analysis in medical field, business, management, accounting and agriculture. I have full access to WOS, Scopus, Medline, PubMed and Clinical trial website where i obtained the articles for research purpose.

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 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

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