Hire the Best Bayesian Statistics Developers

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.

Greetings! I am a PhD candidate in Statistical Machine Learning (ML), and a certified Machine Learning Scientist and Data scientist (Upwork & IBM) with both Master’s and Bachelor’s degrees in Statistics. My strong academic foundation equips me advanced data science and ML workflows. I specialize in developing predictive models for financial markets—particularly in crypto and stock price forecasting—using time series analysis, deep learning, and ensemble methods. My work also involves designing and backtesting algorithmic trading strategies, optimizing model performance under live market conditions, and building interactive dashboards for real-time data visualization and decision support. 🌟 Top Rated on Upwork – Consistent record of client satisfaction and delivery excellence. 🔍 Why Hire Me? ✅ Advanced ML Expertise I leverage cutting-edge techniques to deliver highly accurate and innovative predictive solutions. ✅ Comprehensive Tool Proficiency Programming: Python, R Dashboards & Visualization: Tableau, Power BI, Streamlit Statistical Software: SPSS, STATA, E-Views, Minitab Database & Cloud: SQL, Google Cloud (for scalable, production-ready ML deployments) ✅ Quantitative Research Strength Deep experience in algorithm design and time series forecasting enables trend prediction with exceptional precision. ✅ End-to-End Execution From data ingestion to predictive modeling and visualization, I manage the full pipeline with performance and clarity in mind. ✅ Clear Communication I break down complex ML models and insights into intuitive, actionable formats for stakeholders and non-technical audiences. 💼 What I Deliver: Predictive Modeling Time Series Forecasting & Analysis Interactive Dashboards in Tableau, Power BI, and Streamlit Risk Assessment & Optimization Models Feature Engineering & Model Tuning Visual Analytics & Business Intelligence

A skilled statistician and biostatistician specializing in statistical analysis and healthcare research. Delivered publication-ready results for diverse projects including NHANES arthritis studies , biometric data assessments, and complex analyses in pediatric ICU studies . Proven proficiency in R, SPSS, Stata, SAS,Python, and Excel, and advanced statistical methods, contributing to over 200 completed projects. I have helped clinical researchers, PhD and MSc researchers, and business owners. My expertise includes working with datasets from surveys, clinical studies, and public databases like NHANES, CDC, DHS, and IPUMS. My rigorous analysis is designed to withstand peer review and inform strategic business decisions. Statistical Analysis Services: - Linear, Logistic, and Multinomial Regression Analysis - Repeated Measures ANOVA and Mixed Effects Models - Survival Analysis (Cox PH, Kaplan-Meier) - Survey Data Analysis (SurveyMonkey) - Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) - Longitudinal Data Analysis and Multilevel Modeling - Latent Class Analysis using R or STATA - Power Analysis and Sample Size Calculation - Missing Data Imputation (MICE) Data Mining Expertise: - NHANES (National Health and Nutrition Examination Survey) - CDC Wonder, MEPS and NIHS - DHS (Demographic and Health Surveys) - IPUMS (Integrated Public Use Microdata Series) Software Proficiency: - R (RStudio, tidyverse, ggplot2) - IBM SPSS, Stata, SAS - Python (Jupyter, scikit-learn) - Excel With a Master's degree in Statistics, specializing in Biostatistics, and over 8 years of experience, I have completed more than 200 projects on Upwork, achieving a 96% job success score and an average rating of 4.8. My analyses have contributed to successful journal publications, DNP projects, and strategic business decisions. Ready to start? Send me your dataset or a brief description of your project. Thank you, Daniel

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

We're a team of mathematicians, statisticians, algo-traders and full-stack developers at Insight Fusion Analytics. We build two things well: business intelligence that actually gets used, and trading systems where real money is on the line. 100+ projects completed • 100% Job Success • Top Rated Plus DATA ANALYTICS & BI ─── We've done this across healthcare, insurance, real estate, e-commerce, and operations. Dashboards, automated reporting, competitor analysis, pricing studies, predictive models, data scraping, A/B testing, and research reports. What most statisticians do? Explain their methods. Time series this, Markov chains that, custom probability distributions. What the client thinks? Just tell me what my pricing should be and how to manage my inventory. We skip the lecture and give you the answer. If you're curious about the method, happy to walk through it on a call. Projects worth mentioning: • Lightning detection efficiency analysis for Boltek and ISRO (Boltek's storm tracking systems are deployed by the US Army and Toronto's CN Tower) • Sentiment analysis and review correlation for J Turner Research, the US multifamily industry's leading reputation analytics firm (148,000+ properties tracked) • Booking calendar optimisation for ServiceMarket, Dubai's largest home services marketplace (acquired by Etisalat for $22M) • Pricing and inventory analysis for Triton's Amazon launch Tools: Python, SQL, R, Pandas, NumPy, Scikit-learn, Excel, BigQuery ALGO TRADING & QUANT SYSTEMS ─── This is where we started and it's still what we do best. We're statisticians first, no machine learning hoax. Every output can be verified in Excel. Our USP is pattern recognition. Quick example: say you have a strategy — buy when RSI drops below 30, sell when RSI crosses above 70. Average profit per trade: -4%. Looks bad. But our tools will tell you: run that same strategy only in December, avg becomes +3%, and it covers 12% of all trades. Or between 1pm and 2pm when EMA is hugging, avg is +7% for 21% of trades. We test over a million combinations per strategy before suggesting patterns. (sorry for bringing up pattern recognition again and again, we can't stop talking about it) 70+ trading bots • 60+ backtesting frameworks • 20+ live screener bots We backtest equities, derivatives, and full portfolios. Live execution with kill switches on IBKR, Fyers, Zerodha, NinjaTrader, TradingView, QuantConnect. We've done HFT before but we'll be honest, there are better players than us on Upwork for that. Our USP is the math. Markets: US equities, Indian equities and F&O, Forex, Crypto AI AGENTS & AUTOMATION ─── We build AI agents that do actual work. Not chatbots that answer FAQs. Multi-step workflows where an agent ingests data, makes decisions, takes actions, and reports back. Document extraction pipelines that pull structured data from messy PDFs and emails. Trading signal processors that chain multiple APIs together and fire alerts in seconds. Review response systems that classify sentiment and route to the right person. Every agent ships with error handling, logging, and a kill switch. We're data engineers who added AI to our stack, so the plumbing is solid. Tools: PydanticAI, LangGraph, Claude API, n8n, Make, FastAPI, MCP SPORTS & PREDICTION MARKETS ─── Our favourite. We spend serious time engineering mathematical features with domain experts. Every bit is explainable using formulas. The secret is in the features engineered, not the strategy. All over Upwork we see ML models whose numbers can't be explained. ML applied from the start only raises irrational hopes and takes 6 months to a year before you realise the model isn't a fit. We believe good analysts produce a statistical model that works first, then layer ML on top if it actually improves things. Prediction systems for UFC, soccer, cricket. Edge detection on Polymarket, Kalshi, and similar markets. Full pipeline from data to predictions to signals. WORK WITH US ─── ▸ 30-min consultation ($100) to see if it's a fit, reimbursed in full if we work together ▸ Share your project, we scope it in 24 hours ▸ AI automation audit ($350), we map what can be replaced 7 out of 7 paid consultations have turned into long-term projects. When both sides have skin in the game the conversation is just better. Before reaching out, check out our past work. It helps us all figure out if we're aligned. We're not the cheapest option and we know it. We want you convinced before getting into a long contract. If it fits, let's talk. Make us do the Math.

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.