Hire the Best MultiDimensional Expressions (MDX) Professionals

I help research teams and technical founders build reliable, testable scientific and quantitative software — from research prototypes and backtesting frameworks to production-grade execution systems, simulation engines, and performance optimization. I also conduct independent technical review of quantitative models, frameworks, and research. 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 system. Credibility: PhD Physics (NYU, 2018). Quantitative Research at JPMorgan Chase (derivative pricing, 1M+ LOC C++ library). Max Planck Institute postdoc (general relativistic hydrodynamics simulations on HPC clusters, 1000+ cores). 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 / 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 handling, quality control checks ML and statistical models for time-series signals (proper time-series CV, no leakage) ▸ Scientific Computing & Research Tooling Custom software and research tooling for research-driven teams across physics, chemistry, biology, engineering and beyond — where the underlying problem is mathematical or computational. - Custom numerical solvers (finite volume / finite difference, spectral methods, particle methods), with stability and convergence analysis - Optimization engines (Bayesian optimization, gradient-based, evolutionary) for experimental design, formulation, and parameter search - Simulation frameworks for physical, chemical, and biological systems — from prototype to production-grade - Scientific data pipelines: ingestion, transformation, quality control, reproducible workflows - Verification and validation: benchmarks, unit tests, 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 Review of implementation against specification: numerical stability, edge cases, and code-to-specification fidelity ▸ HPC & Performance Engineering Performance engineering for large-scale scientific and ML workloads Distributed computing (MPI/OpenMP/CUDA), GPU optimization, memory and I/O tuning, parallel architectures, 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 engineering: I turn research prototypes into tested, reproducible production code - De-risking: I find failure modes early (leakage, overfitting, edge cases, scaling bottlenecks) - Maintainability: clean architecture, docs, and 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 details 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 or implementations against specification - Market data pipeline and reproducibility upgrades - Performance optimization of slow Python/C++ codebases - Distributed training, GPU optimization, and inference serving for AI/ML workloads - Custom scientific or industrialized simulation or numerical software - Custom internal R&D tools for research labs and technical teams If this sounds like a fit, message me with a detailed brief with your current stack and success criteria — I'll let you know how I can help.

Proteomics and metabolomics analysis. Bioinformatics pipelines. PhD-level R programming. I'm a PhD biochemist with deep expertise in omics data analysis and advanced R development. I work with biotech companies, research labs, and clinical organizations who need specialized expertise on-demand—without the overhead of a full-time hire. My background: - PhD Biochemistry (UBC, 2024) with focus on proteomics and mass spectrometry - Production experience: Built and optimized a 1TB mass spec data pipeline processing 30+ cell line samples with 3000-10,000 features per file - Clinical expertise: Analyzed 300+ patient datasets for biomarker discovery and clinical outcomes correlation - Published research: 7 peer-reviewed papers (proteomics, immunology, cancer research) - Advanced R programming: Custom analysis workflows, statistical methods, machine learning in R (PCA, clustering, random forests) What I offer: - Proteomics/metabolomics data analysis (raw data → publication/deliverable-ready) - Bioinformatics pipeline development and optimization - Statistical analysis (t-tests, ANOVA, regression, survival analysis, multivariate methods) - Publication-quality visualization (heatmaps, volcano plots, PCA plots, custom graphics) - GraphPad Prism analysis (clinical trials, dose-response, Kaplan-Meier curves) - Results interpretation and scientific reporting - R training and troubleshooting for your team Not my domain: - Regulatory work (SDTM/ADaM), single-cell ATAC-seq, deep learning, business analytics. I work efficiently, communicate clearly, and understand the difference between elegant code and reproducible science. Let me take your analysis burden off your plate.

I work on genomics and biology projects, the kind where you have raw sequencing data, mass spec output, or protein sequences and you need someone to turn it into results you can publish or act on. I've done this across more than 67 projects on Upwork and through my PhD and postdoc, so I know what a clean analysis looks like and I know the shortcuts that cause problems later. Quick background: Actually postdoc in population genetics. PhD in bioinformatics and molecular evolution at Paris-Saclay (CNRS), where I worked on plant-bacteria interactions. Master's in bioinformatics from Sorbonne, and bachelor's in computer science from Descartes. I've been programming for over 10 years, mostly Python, R, and Bash, though I can work in Java, C, or JavaScript if a project calls for it. Eight years of building data pipelines specifically. What I do: Genomics and population genetics. GWAS, co-GWAS (PLINK2, Firth regression), population structure analysis (PCA, ADMIXTURE, pairwise FST), ancient DNA work (qpAdm, qpWave, ADMIXTOOLS2), variant calling and annotation. I've run these on human, plant, pathogen, and ancient samples. Genome assembly and annotation: De novo assembly from Nanopore or Illumina, gene prediction, functional annotation, genome comparison. Bacteria, plants, insects, human. I've assembled all of them at some point. RNA-seq: Differential expression from raw reads, co-expression networks, pathway enrichment. Full pipeline or just the analysis part, depending on what you need. Structural bioinformatics: AlphaFold2 batch runs, FoldSeek for structural comparisons, molecular docking with ClusPro, AutoDock Vina, or HADDOCK. I do the visualization too (PyMOL, ChimeraX), and create publication-ready figures, not just screenshots. Phylogenetics and molecular evolution: IQ-TREE2, RAxML, selection tests, gene family evolution, protein evolution. Proteomics and lipidomics: Mass spec data processing through to statistical analysis and biological interpretation. Pipelines: I build workflows, documented and tested with example data. Your team should be able to rerun them without me. Writing and code review: Methods sections, technical reports, manuscript figures. I also debug and review existing pipelines. If your code or script doesn't work, I'll fix it and tell you why it broke. Everything comes with commented code and R Markdown or Jupyter reports. Figures are made in ggplot2 or matplotlib and ready for submission. 100% Job Success score across 65+ projects. I reply the same day. "If you need a bioinformatics expert who's not only insanely skilled but also great to work with, Dr. Amira is the one. Would 100% work with her again!" (Client) "Amira delivered a thorough and well-structured technical review, including worked examples and code snippets that were very useful for demonstrating enablement. Her analysis was detailed, thoughtful, and delivered on time. I highly recommend her for bioinformatics and technical review projects." (Recent client) Drop me a message with your data and what you're trying to do. I'll get back to you with a plan and timeline.

Do you need someone who can handle your entire data journey? From collecting data, to analysing it, to automating the processes around it, and finally presenting it in a stunning, interactive dashboard? You've found that person. I'm Catherine, a data analyst and statistician with deep expertise across the full data pipeline. I don't just run numbers. I help you collect, clean, analyse, automate, and visualise your data in a way that drives real decisions. What I bring to your project: 📊 Statistical analysis & research: Hypothesis testing, regression, SEM, survival analysis, psychometrics/scale validation, and econometric modelling using SPSS, R, Stata, Python, Jamovi, and JMP. I've contributed to peer-reviewed journal publications through my statistical work on surgical databases (NSQIP). 🔍 Qualitative research: Thematic and content analysis using NVivo, MAXQDA, and Atlas.ti for interviews, focus groups, and open-ended survey data. ⚙️ Automation & workflow design: I build end-to-end automated pipelines using Make and Google Apps Script, including integrations with Stripe, Google Forms, Google Sheets, landing pages, and more. Recent clients have seen their manual data workflows fully automated. 📈 Dashboards & data visualisation: Interactive executive dashboards in Power BI and Looker Studio, tailored for marketing teams, operations, healthcare, and finance. I turn raw data into clear, compelling visual stories. 📋 Survey design & data collection: End-to-end survey projects using Google Forms, Typeform, and Qualtrics, including automated data pipelines from form submission to clean, analysis-ready datasets. I've worked across yachting, healthcare, finance, and academia, and I also offer tutoring in SPSS, R, and Stata for students and professionals. Let's talk about how I can support your project.

Medical doctor (MBBS) and experienced AI content evaluator with over 18 months of hands-on data annotation and content moderation work at Outlier. I specialise in evaluating AI-generated text, images, and multimedia for quality, safety, accuracy, and policy compliance, which is exactly the work that makes frontier AI models better and safer. What I bring to AI evaluation projects: Assessed and moderated AI outputs across text, image, and conversational formats, flagging harmful, misleading, or policy-violating content with structured reasoning Applied annotation rubrics and ranking frameworks to evaluate AI responses by relevance, helpfulness, and safety risk Delivered clear, actionable written feedback directly to development teams to support iterative model improvement Documented findings using Google Sheets and Notion; consistently met daily review targets in a fully remote environment Why my medical background matters for this work: A medical degree trains you to analyse complex information under pressure, apply structured frameworks to ambiguous situations, and communicate findings precisely. These are exactly the cognitive skills that separate good AI evaluators from great ones — especially for health, science, and high-stakes content. I'm a strong fit if you need: ✔ AI content moderation & safety evaluation ✔ Text and image annotation ✔ Video content evaluation and QA ✔ LLM response ranking and RLHF-style feedback ✔ Medical or scientific content review ✔ Social media content policy assessment

I help research teams and biotech companies turn complex clinical and genomic data into clear, publication-ready insights. With a unique background as a former dentist, I don't just see the numbers; I understand the clinical context behind them. I specialize in designing and executing hypothesis-driven research projects. My work begins with processed genomic or clinical data (e.g., from TCGA, GEO, or your own lab), from which I generate deep biological insights using advanced statistical and machine learning models in R. My Core Services: Bioinformatics & Genomics: Downstream analysis of large-scale datasets (TCGA, GEO), gene expression & RNA-Seq analysis, and biomarker discovery. Clinical Biostatistics: Survival analysis (Kaplan-Meier, Cox Models), clinical trial data analysis, longitudinal studies, and analysis of real-world data (EHR). Machine Learning: Building and validating predictive models in R (tidymodels, caret) to stratify patients or predict clinical outcomes. Data Visualization & Reporting: Creating clear, publication-quality figures (ggplot2) and interactive dashboards (Power BI) that tell a compelling story. If you need a research partner who can handle the entire high-level data analysis portion of your project—from hypothesis to publication—let's connect.