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Data scientist, specializing in delivering AI and analytics-driven solutions for Marketing, Working around topics related to CRM, personalization, and audience building, my professional journey includes significant achievements with both large retail brands such as 'ALDO Shoes' and dynamic startups like 'Tastet.ca'. I am driven by the challenge of transforming complex data into actionable insights that propel business growth and customer satisfaction. My expertise lies in leveraging the power of advanced machine learning techniques including GPTs to develop applications that enhance user experiences and operational efficiency. From streamlining e-commerce through AI-powered quality assurance tools to fostering culinary discovery with personalized recommendation systems, my work so far encapsulates a commitment to innovation and precision. Join me in leveraging cutting-edge technology to uncover opportunities, optimize your AI applications, and tell compelling stories with data. Let's collaborate to ensure that your data isn't just big, but profoundly insightful and strategically impactful. I am new to Upwork, with a proven track record of projects offline. Please visit aidotai.com

Machine learning engineer for time series and sensor data. I build and validate predictive pipelines on biosignals and wearables, IoT streams, energy series, and time-indexed text. Every engagement runs end to end, from raw data through validated model to a report and codebase your team can run without me. WHAT THE WORK LOOKS LIKE Signal processing and feature extraction from noisy multichannel data. Forecasting, classification, and anomaly detection under time-aware and participant-level cross-validation. Data pipelines that turn raw sensor streams or document archives into analysis-ready, versioned datasets. Statistical testing and signal analysis when the question is "is this effect real" rather than "build me a model." Deep learning where it earns its place and classical methods where they don't need replacing. Results reported with uncertainty, not just a headline metric, and delivered as reproducible, containerized Python code, deployed where the engagement calls for it, with technical reports written for both engineers and decision-makers. The center of my practice is time series and sensor data. I've applied it across biotech and wearables, finance, energy, and industrial IoT, and the validation discipline is the same in every one: the wrong split quietly invalidates the result, whichever industry it's in. SELECTED OUTCOMES ◆ Improved heart-failure readmission prediction by over 20% AUC from chest-worn accelerometer data, validated with patient-level cross-validation and confidence intervals, so the Series A medtech team had a result they could defend to clinical and investor audiences. ◆ Turned 18 years of editorial coverage into a quantitative geopolitical index for institutional finance, with an entity-to-equity layer carrying a point-in-time firewall (zero look-ahead events across 46,000 article-entity rows) and lead-lag relationships validated in-sample against country ETFs. Licensed to an institutional quantitative fund. ◆ Built a production-ready IoT demand-forecasting system: 10–13% MAPE under walk-forward validation and a consumption-event detector at 93.2% accuracy, handed off with the PoC-to-MVP deployment roadmap. ◆ Benchmarked seven deep-learning forecasters against classical baselines for solar irradiance under nested time-series cross-validation, with Diebold-Mariano tests and multiple-comparison correction. The finding the client could act on: deep models earn their cost at 1-hour and multi-day horizons, but at day-ahead a seasonal baseline is within noise, so the recommendation was a cheaper model for that horizon. Publication-grade report with full statistical appendix. ◆ Audited an inherited deep time-warping model and found it was optimizing the wrong objective; re-implemented it with an attention-based architecture, multi-seed evaluation, and confidence intervals on retrieval metrics, turning an unverifiable prototype into a reportable one. ◆ Designed and ran a falsifiable research pilot on sleep EEG testing whether model-error divergence precedes classical early-warning signals, with residual diagnostics and sensitivity analysis; the client called it "exceptional professionalism, rigor, and methodological clarity." HOW ENGAGEMENTS RUN Fixed-price, scoped milestones. Larger projects start with a paid discovery phase that produces a technical plan, data assessment, and acceptance criteria before any modeling begins. Shorter formats for well-defined questions: one-to-three-day signal or statistical analyses, technical reviews of an existing pipeline or model, and consultations on data strategy, feasibility, and what to build first. TOOLS Python (NumPy, SciPy, pandas, scikit-learn, statsmodels, PyTorch, LightGBM, XGBoost, Optuna, RDKit), signal processing (filtering, EMD decomposition, spectral and time-frequency features), pvlib, Hugging Face Transformers, SQL, Docker, FastAPI, AWS Tell me what you're trying to predict, understand, or decide, and what data you have or plan to collect. I'll identify the most practical first step, and if it's a fit, I'll scope it clearly.

UML, Java programming, C,C++,C# programming, Unity, Flutter, Android programming, Artificial intelligence, Machine Learning,

𝗜 𝗱𝗲𝘀𝗶𝗴𝗻 𝗮𝗻𝗱 𝗱𝗲𝗽𝗹𝗼𝘆 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗰𝗿𝗲𝗮𝘁𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝗺𝗽𝗮𝗰𝘁. I’m a Senior Machine Learning Engineer specializing in Agentic AI, Generative AI, LLM-based systems, Deep Learning, Computer Vision (YOLO), Predictive Analytics, and production-grade MLOps deployment. I hold an MSc in Computer Science and AI with 8+ years of experience helping startups, fintech platforms, healthcare teams, and growing enterprises move from AI idea to production deployment, reliably and at scale. I don’t build experiments. I build systems that integrate cleanly, perform consistently, and deliver long-term value. 𝗪𝗵𝗮𝘁 𝗜 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲 𝗜𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 & 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 • Multi-agent system design & orchestration • Autonomous decision-making workflows • LLM agents for task automation & planning • Human-in-the-loop agent integration • LangChain, AutoGen & CrewAI implementation 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴, 𝗟𝗟𝗠 & 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 • Retrieval-Augmented Generation (RAG) • AI copilots & intelligent assistants • Knowledge-base integrated chatbots • Structured data grounding & vector search • Evaluation, guardrails & performance optimization 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 & 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 • Demand forecasting & time-series modeling • Fraud detection & risk scoring • Customer churn & behavioral prediction • Feature engineering & model optimization 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 & 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 • YOLO-based object detection (YOLOv5 / YOLOv8) • Image classification & video intelligence • Custom dataset training & small-object detection • Edge optimization & quantization-aware deployment 𝗠𝗟𝗢𝗽𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 • End-to-end ML pipelines • Model monitoring & drift detection • CI/CD for machine learning • Scalable cloud deployment (AWS, Azure, GCP) • API integration & system architecture design 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗘𝗺𝗮𝗶𝗹 𝗙𝗶𝗹𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺 (𝗥𝗲𝗮𝗹 𝗘𝘀𝘁𝗮𝘁𝗲) • Architecture: Multi-agent system using Claude for email classification, document analysis, and validation. • Tech: RAG for context-aware folder retrieval, structured prompt engineering, and strict output schemas. • Impact: Automated Gmail-to-Google Drive filing with human-in-the-loop review for edge cases, significantly reducing manual workload. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 & 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 • Architecture: Advanced LLM orchestration for faithful PDF layout preservation during translation. • Tech: Expert-level Python, validation workflows, and complex document parsing. • Impact: Delivered high-fidelity product manual translations maintaining original formatting and font integrity. 𝗔𝗜𝗢𝗡 - 𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗕𝟮𝗕 𝗦𝗮𝗮𝗦 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 • Architecture: Scalable multi-tenant operating system unifying CRM, workflow automation, and commerce. • Tech: AI-first design for cross-channel communication (Instagram, WhatsApp, Messenger) and sales automation. • Impact: Centralized customer data and engagement into a single intelligent platform, eliminating disconnected tools. 𝗦𝗲𝗹𝗲𝗰𝘁𝗲𝗱 𝗜𝗺𝗽𝗮𝗰𝘁 • Reduced operational waste by 25% through advanced forecasting systems • Improved fraud detection precision for fintech platforms • Built enterprise-grade AI assistants integrated with structured knowledge bases • Deployed real-time object detection systems in production environments • Designed scalable ML infrastructure supporting long-term model reliability 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗦𝘁𝗮𝗰𝗸 • Python | TensorFlow | PyTorch | Scikit-learn • Agent Frameworks: LangChain, AutoGen, CrewAI, LlamaIndex • Cloud Platforms: AWS, Azure, Google Cloud • MLOps: Docker, Kubernetes, MLflow • NLP, Computer Vision, Generative AI & Predictive Modeling • N8N, Zapier, Airtable Automations, Make (Integromat), Notion Automations, Retool 𝗛𝗼𝘄 𝗜 𝗪𝗼𝗿𝗸: Every engagement begins with defining measurable KPIs and a clear architecture plan. From there, I design the right combination of data pipelines, models, and infrastructure to ensure the solution performs reliably in production, not just in development. I work best with teams building serious products who need AI engineered properly from day one: secure, scalable, and maintainable. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝘂𝘀 𝗮𝗻𝗱 𝗻𝗲𝗲𝗱 𝘀𝗲𝗻𝗶𝗼𝗿-𝗹𝗲𝘃𝗲𝗹 𝗔𝗜 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻, 𝗜’𝗱 𝗯𝗲 𝗴𝗹𝗮𝗱 𝘁𝗼 𝗱𝗶𝘀𝗰𝘂𝘀𝘀 𝘆𝗼𝘂𝗿 𝗴𝗼𝗮𝗹𝘀.

I build AI agents, automations, and RAG systems that ship to production - not demos that die in a notebook. As the founder of BeautyBuzz AI (a live SaaS with real users), I've taken AI products through the full cycle: idea → build → deploy → scale. I'm a full-stack developer with deep AI expertise, which means you get one person who can design the model, build the backend, and ship the app - no handoffs, no gaps. What I build for clients: • AI Agents & Multi-Agent Systems — LangChain, LangGraph, CrewAI; tool-calling, memory, orchestration • AI Automation & Workflows — n8n, Make, Zapier + OpenAI/Claude; connect your CRM, email, Slack, docs so work runs itself • RAG & Knowledge Assistants — chat over your documents/data with accurate, cited answers (vector DBs, hybrid retrieval) • LLM Fine-Tuning & Integration — OpenAI, Anthropic Claude, Gemini, Llama; prompt engineering, evals, cost/latency optimization • Full-Stack AI Products & MVPs — Python/FastAPI backends, clean databases, cloud deployment (AWS/GCP/Azure), Docker, CI/CD Why clients hire me: ✅ 100% Job Success Score and $60K+ earned on Upwork ✅ Founder of a real, deployed AI SaaS — I think about your business outcome, not just the code ✅ Recent 5-star work: agentic RAG systems, document-processing automation, a high-performance LLM inference engine, and a 150-hour PostgreSQL + Python app Tech I work with daily: Python, FastAPI, LangChain, LangGraph, OpenAI & Claude APIs, MCP, Pinecone/pgvector, Hugging Face, PyTorch, TensorFlow, Docker, AWS, n8n, SQL, React/Next.js. If you need an AI agent, an automation that saves hours every week, or a production-ready AI feature built properly the first time, send me a message or invite me to your job - I reply within hours and I'll tell you honestly what's worth building.

Professional researcher and data scientist with health, human resources, and social sciences expertise. More than 16 years freelance experience. Deliverable focused. A "Top Rated" freelancer, Joanne is in the top 10% of all Upwork users. Top Rated plus designation is only awarded to those with experience managing large scale project. When you choose to work with a Top Rated professional, you can be confident that you’ve partnered with a professional that has a track record of providing great service to clients on Upwork. My goals are: 1. to use technology to streamline how we gather and analyze information; 2. to use my research design, analytics and informatics experience to make your information meaningful and actionable; 3. to use my exceptional writing about statistics, research, and technical information to take your deliverables to the next level so you can demonstrate results. Talk to me about your data challenge or marketing research questions; about designing your online surveys and data collection tools; or about managing your consumer online panel. With data in hand, talk to me about data mining, statistical analyses, interpreting your results, and about packaging it all in a concise and professional report. I am interested in applying my expertise to short term projects using tools such as Alchemer surveys as well as analytics using DisplayR, MS Excel, Power Point, SPSS, Claude, ChatGPT, and Adobe Creative Suite.