[{"content":"Overview I support research and health innovation teams that need stronger analytical workflows around biological and clinical data. This service sits at the intersection of data science, reproducible research, and health-domain problem solving.\nThe focus is practical: improve data quality, structure the analysis pipeline, document assumptions, and produce outputs that are easier to validate, communicate, and reuse.\nAreas of support Clinical and observational dataset preparation Reproducible analysis pipelines in Python or R Feature engineering for structured biomedical data Exploratory analysis for cohorts, outcomes, and risk factors Support for omics-oriented workflows and downstream interpretation Visualisation and reporting for researchers and decision-makers Prototype decision-support analytics for health applications Typical deliverables Cleaned and documented analysis-ready datasets Reproducible notebooks or scripts QA checks and data dictionaries Statistical or ML exploration reports Visual summaries for presentations, papers, or internal reviews Ideal clients Research groups and academic labs Digital health or medtech startups Clinical innovation teams Organisations building analytics around biomedical or patient data Best fit This service is a good fit when you need someone who can bridge rigorous analytical work with real-world project delivery, especially in settings where health data, interpretability, and reproducibility matter.\n","permalink":"https://www.marcusrb.com/services/bioinformatics/","summary":"Applied bioinformatics and health-data support for research groups, startups, and clinical innovation teams working with reproducible analytical workflows.","title":"Bioinformatics and Health Data Support"},{"content":"Overview I help companies design and implement measurement systems that produce reliable data for marketing, product, and business reporting. This includes GA4 setup, Google Tag Manager development, event taxonomy design, debugging, and governance.\nThe objective is not only to make tags fire, but to make the data model coherent enough for reporting, experimentation, attribution, and downstream analysis.\nWhat I can help with Measurement plan and event taxonomy design GTM container architecture and naming conventions GA4 property configuration and event implementation Ecommerce and lead-generation tracking Form, scroll, video, and CTA interaction tracking Conversion, audience, and funnel setup Cross-domain tracking and consent-aware implementations Audit and remediation of broken or duplicated tracking Typical deliverables Tracking specification aligned with business goals GTM container build or refactor GA4 event and conversion setup QA plan using preview, debug, and browser-based validation Documentation for internal marketing or engineering teams Working style I usually start with the business questions first, then map those questions to events, parameters, and reporting needs. That avoids the common failure mode of collecting large amounts of unusable tracking data.\nBest fit This service is a good fit when you are migrating to GA4, cleaning up an inherited GTM container, launching new funnels, or building a measurement setup that engineering and marketing teams can actually maintain.\n","permalink":"https://www.marcusrb.com/services/ga4-gtm-development/","summary":"Measurement architecture, GA4 implementation, GTM development, and tracking audits for marketing, product, and data teams.","title":"GA4 and GTM Development"},{"content":"Overview I help organisations identify where machine learning or AI will create measurable value, and where a simpler approach is the better decision. The work starts with business objectives and operational constraints, not with models for their own sake.\nProjects can cover classical machine learning, predictive analytics, decision support systems, NLP workflows, or AI-enabled product features. Depending on the case, I can support strategy, prototyping, technical delivery, or model operationalisation.\nWhat the engagement can include Use-case assessment and feasibility analysis Data audit, feature design, and dataset preparation Baseline modelling and benchmark comparison Model evaluation with business-relevant metrics Explainability, risk review, and stakeholder communication API, workflow, or product integration planning Handover documentation and team enablement Typical use cases Demand forecasting and propensity modelling Lead scoring and conversion optimisation Customer segmentation and recommendation systems Document classification and NLP pipelines Clinical or operational decision support Internal AI workflows for content, search, or process automation Delivery principles Start with a narrow, testable problem Use interpretable methods when governance matters Validate against operational constraints early Design for maintenance, not just a demo Best fit This service is a good fit when you need a senior technical partner to scope an ML or AI initiative, build a working prototype, or move an existing model closer to production.\n","permalink":"https://www.marcusrb.com/services/machine-learning-ai/","summary":"End-to-end consulting for ML and AI initiatives, from use-case validation to production-ready workflows and stakeholder adoption.","title":"Machine Learning and AI Consulting"},{"content":"Overview I design and deliver training programmes for organisations that need more than generic slide decks. The goal is to help teams apply data, analytics, and AI in their day-to-day work, whether the audience is technical, managerial, or mixed.\nMy teaching work includes in-company programmes, executive education, guest lectures, master-level modules, and custom workshops. Sessions can be delivered in person, live online, or in blended formats.\nTopics I teach Data literacy for business and marketing teams Analytics strategy and KPI design SQL, data modelling, and data preparation Power BI, Tableau, and dashboard design Python for data analysis and machine learning Supervised learning, model evaluation, and experimentation Applied AI, LLM use cases, and responsible adoption GA4, GTM, and measurement planning Typical training outcomes A curriculum adapted to your team level and business context Slides, labs, exercises, and worked solutions Office hours or follow-up sessions to resolve real implementation blockers A documented learning path that internal teams can reuse after the engagement Formats Intensive workshops from a half day to several days Multi-week internal academies Masterclasses for leadership teams University or business-school teaching modules Train-the-trainer sessions for internal enablement teams Best fit This service is a good fit when your team needs to build capability quickly, align on common methods, or move from theory to applied execution in analytics, BI, machine learning, or AI.\n","permalink":"https://www.marcusrb.com/services/training/","summary":"Practical training programmes for companies, universities, and business schools in analytics, machine learning, AI, BI, and experimentation.","title":"Training and Executive Education"},{"content":"Programme Week Topic 1–4 Python, statistics, data wrangling 5–8 Supervised learning, model evaluation 9–12 Deep learning, NLP, computer vision 13–16 MLOps, GCP deployment, capstone Materials Lecture notes and notebooks are available to enrolled students via the course portal.\n","permalink":"https://www.marcusrb.com/courses/ml-bootcamp/","summary":"\u003ch2 id=\"programme\"\u003eProgramme\u003c/h2\u003e\n\u003ctable\u003e\n\t\u003cthead\u003e\n\t\t\t\u003ctr\u003e\n\t\t\t\t\t\u003cth\u003eWeek\u003c/th\u003e\n\t\t\t\t\t\u003cth\u003eTopic\u003c/th\u003e\n\t\t\t\u003c/tr\u003e\n\t\u003c/thead\u003e\n\t\u003ctbody\u003e\n\t\t\t\u003ctr\u003e\n\t\t\t\t\t\u003ctd\u003e1–4\u003c/td\u003e\n\t\t\t\t\t\u003ctd\u003ePython, statistics, data wrangling\u003c/td\u003e\n\t\t\t\u003c/tr\u003e\n\t\t\t\u003ctr\u003e\n\t\t\t\t\t\u003ctd\u003e5–8\u003c/td\u003e\n\t\t\t\t\t\u003ctd\u003eSupervised learning, model evaluation\u003c/td\u003e\n\t\t\t\u003c/tr\u003e\n\t\t\t\u003ctr\u003e\n\t\t\t\t\t\u003ctd\u003e9–12\u003c/td\u003e\n\t\t\t\t\t\u003ctd\u003eDeep learning, NLP, computer vision\u003c/td\u003e\n\t\t\t\u003c/tr\u003e\n\t\t\t\u003ctr\u003e\n\t\t\t\t\t\u003ctd\u003e13–16\u003c/td\u003e\n\t\t\t\t\t\u003ctd\u003eMLOps, GCP deployment, capstone\u003c/td\u003e\n\t\t\t\u003c/tr\u003e\n\t\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2 id=\"materials\"\u003eMaterials\u003c/h2\u003e\n\u003cp\u003eLecture notes and notebooks are available to enrolled students via the course portal.\u003c/p\u003e","title":"Machine Learning Engineering Bootcamp"},{"content":"Overview HerHeart is a clinical decision support system (CDSS) that stratifies cardiovascular risk in women using machine learning on EHR data.\nMethods Feature engineering on structured EHR Gradient boosting (LightGBM) + calibration SHAP explainability for clinicians Status Active development · B2B pilot with 2 health systems in Spain.\n","permalink":"https://www.marcusrb.com/projects/herheart/","summary":"\u003ch2 id=\"overview\"\u003eOverview\u003c/h2\u003e\n\u003cp\u003eHerHeart is a clinical decision support system (CDSS) that stratifies cardiovascular risk in women using machine learning on EHR data.\u003c/p\u003e\n\u003ch2 id=\"methods\"\u003eMethods\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eFeature engineering on structured EHR\u003c/li\u003e\n\u003cli\u003eGradient boosting (LightGBM) + calibration\u003c/li\u003e\n\u003cli\u003eSHAP explainability for clinicians\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"status\"\u003eStatus\u003c/h2\u003e\n\u003cp\u003eActive development · B2B pilot with 2 health systems in Spain.\u003c/p\u003e","title":"HerHeart — cardiovascular risk AI for women"},{"content":"Feel free to reach out for consulting, teaching collaborations, or academic projects.\n","permalink":"https://www.marcusrb.com/contact/","summary":"\u003cp\u003eFeel free to reach out for consulting, teaching collaborations, or academic projects.\u003c/p\u003e","title":"Contact"}]