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I work as a forward-deployed AI engineer — embedded directly with your team to design, build, and ship production AI systems, not just advise on them. Most AI engineers can wire up an API; few understand data at depth. I do — and that combination is the whole point. My background spans 20+ years at PricewaterhouseCoopers (PwC) and Publicis Sapient — seventeen years building software, data, and machine-learning systems, and the last three focused entirely on generative AI. That lets me bridge business strategy and technical execution for Fortune 500s and growth-stage companies. Clients span ADT, Aflac, AT&T, Capital One, Carnival, Lumen (formerly CenturyLink), Eli Lilly, Fanatics, HBO, IBM, Lennar, NBCUniversal, USAA, Visa, Wells Fargo, and Worldpay. Degrees in Computer Science and Applied Statistics, plus an MBA. 𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝗱 Agentic AI systems — autonomous agents that reason, make multi-step decisions, and take real-world action through tools and APIs. I own the full design: LLM selection, RAG architecture, MCP server development, multi-agent orchestration, and production deployment on AWS, Azure, or GCP. The data-science layer comes built in — pipelines, predictive models, and evaluation — so agent decisions are trustworthy and measurable, not black boxes. 𝗪𝗵𝘆 𝗺𝘆 𝗯𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 𝗶𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 Most AI engineers don't understand data at depth. My foundation spans the full data-science stack — statistical inference, supervised and unsupervised machine learning, time-series, propensity scoring, customer segmentation, and marketing-mix and incrementality modeling — across financial services and fintech, insurance, telecom, healthcare and life sciences, media and entertainment, retail and e-commerce, consumer goods, and hospitality and travel. That depth changes how I build AI: models that are properly validated, pipelines designed for data quality, and outputs judged against real business metrics — not benchmark scores. 𝗦𝗲𝗹𝗲𝗰𝘁𝗲𝗱 𝘄𝗼𝗿𝗸 — 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 • ContactOS — a multi-tenant AI customer-service platform in production across voice (Twilio, Deepgram, ElevenLabs), WhatsApp, web chat, and outbound campaigns. LangGraph ReAct agents call live customer APIs in real time; per-tenant configuration via a runtime registry (a new client is a new directory, not a code change); human escalation over a WebSocket dashboard. • Replaced a specialty retailer's HubSpot CRM with a production FastAPI + React platform in weeks — built with agentic AI development tooling throughout, with AI email summarization and a shared-database architecture with the client's analytics portal, live at a fraction of the prior license cost. • Two-phase agentic engineering triage — a fast LLM classifier routes inbound tickets in under 10 seconds, then an investigation agent autonomously traces the codebase and posts structured root-cause findings to Jira. • End-to-end RAG content engine — ingests YouTube transcripts, PDFs, Kindle highlights, and web articles, then generates channel-specific content grounded in the author's own voice (not generic AI output), auto-publishing to WordPress with a companion audio file. 𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘂𝗻𝗱𝗲𝗿𝗻𝗲𝗮𝘁𝗵 The same modeling rigor I bring to making agent decisions measurable: • Telecom propensity — scored 6.1M customers across 200+ behavioral indicators into calibrated propensity tiers, prioritizing the highest-likelihood segments to steer self-service adoption and call deflection. • Supplemental insurer media-mix — modeled a $171M media portfolio with adstock and saturation, producing per-channel ROI with credibility intervals to guide budget reallocation. • CPG segmentation — an RFM + cohort-LTV engine on Databricks classifying 83K customers weekly, driving segment-level targeting across discounting, channel, and reactivation. 𝗖𝗼𝗿𝗲 𝘀𝘁𝗮𝗰𝗸 Python · SQL · R · PySpark | LangGraph · LangChain · CrewAI · n8n · MCP Servers | Anthropic Claude · OpenAI · Ollama | Pinecone · Qdrant · ChromaDB · pgvector · mem0 | Twilio · Deepgram · ElevenLabs · Whisper | FastAPI · React · TypeScript · Docker · GitHub Actions | PostgreSQL · MongoDB · Snowflake · Redshift | dbt · Databricks | XGBoost · scikit-learn · MLflow | Tableau · Power BI · ECharts | AWS · Azure · GCP 𝗛𝗼𝘄 𝗜 𝘄𝗼𝗿𝗸 I take ownership of delivery — I scope, architect, build, and deploy, and communicate clearly with technical and business stakeholders throughout. Whether you need a solo IC or a lead to drive a small team, the approach is the same: embedded, accountable, and shipping.

I help postgraduate students, doctoral candidates, researchers, and journal authors improve complex academic documents into clear, well-structured, submission-ready work. I work on thesis editing, dissertation editing, literature reviews, systematic literature reviews, research papers, journal manuscripts, proposals, citation correction, and university/journal formatting. My focus is on structure, argument flow, academic tone, clarity, referencing, formatting, and overall presentation. If you send me an invitation or message, I can review your document and tell you exactly what needs improvement before we start. A little more about me: I help clients improve academic work across thesis editing, dissertation editing, literature reviews, research papers, research proposals, proofreading, citation correction, and journal/university formatting. If your document is unclear, repetitive, weakly structured, poorly referenced, AI-sounding, or not aligned with academic guidelines, I’ll clean it up and make it stronger, clearer, and more professional. • Top Rated academic writer/editor with 100% Job Success Score and all 5-star reviews • Experience with theses, dissertations, literature reviews, research papers, essays, and proposals • Strong support for structure, critical analysis, argument flow, citations, formatting, and academic tone • APA, MLA, Harvard, Chicago, IEEE, and journal-specific formatting • Clear communication, deadline-focused delivery, and polished professional documents If you’re looking for someone who can improve the academic quality, structure, and presentation of your work without making it sound generic or AI-written, I can help. ✎ EXPERTISE: Academic Editing | Academic Writing | Thesis Editing | Dissertation Editing | Literature Review | Research Paper | Research Proposal | Manuscript Editing | Proofreading | APA Formatting | Harvard Referencing | IEEE Formatting | Citation Editing | Journal Article Editing | Turnitin Report | Microsoft Word

I am a passionate data Scientist and Machine Learning enthusiast. I can help you in the following things. * I have experience in Data cleaning, Data Labeling, Data Manipulation * Training and fine-tuning Machine Leaning Models * Python programing * Deep Learning * Problem solving

During my research, I have applied advanced statistical and machine learning tools to analyze images. I am comfortable with Big Data: my work required me to routinely process data several 100 GB in size. My expertise is in data mining and feature extraction, especially when it comes to noisy data.

Top-Rated Freelancer On Upwork I have over a decade of cumulative experience in the field of Data Science, Deep Learning, and Machine Learning Python as an Expert I'm very passionate about OpenAI, GPT-3/4, ChatGPT, and Stable Diffusion tech. I am an award-winning technology leader specializing in Machine Learning, Natural Language Processing, and Advanced Analytics. I am responsible for the design, development, and evaluation of text mining systems both in commercial enterprise and research contexts. These systems involve the management and efficient analysis of extremely large datasets employing cutting-edge learning algorithms and heavy-weight distributed resource management platforms. Information extraction, multi-document summarization, conversation analysis, and question answering technologies. Specific areas of expertise and interest include Deep Learning, Natural Language Processing (NLP), Machine Learning, Data Mining, Information Extraction, Question Answering, Genomics, Consumer Recommender Systems and Personalised Filtering.

Data Scientist & Statistician of experience in R, Python, SPSS,Excel،Power BI and SQL. I specialize in machine learning, predictive modeling, biostatistics, clustering, classification, and data visualization. R Packages: tidyverse, tidymodels, dplyr, ggplot2, caret, randomForest, glmnet, xgboost, nnet, lme4, forecast, data.table, readr, stringr, Shiny, Quarto, R Markdown and much more Python Packages: pandas, numpy, scikit-learn, statsmodels, matplotlib, seaborn, plotly, TensorFlow, Keras, PyTorch, XGBoost, LightGBM, CatBoost. Other Tools: SPSS, SAS, Minitab,Power BI, PASS, Microsoft Excel/Office, SQL, Power BI etc... I deliver clean, reproducible, and decision-ready analytics — from statistical modeling and survey analysis to machine learning pipelines and dashboards.

Data Scientist | Software Developer Data Scientist & Software Developer splitting my time between living in Texas and SG (UTC +8). I build production-ready data systems and ML infrastructure that teams can interpret, maintain, and lean on. My work sits at the intersection of machine learning, cloud infrastructure, and agent-driven automation. Whether it's a predictive model, a multi-stage data pipeline, or an AI-powered orchestration system, I build things that are clean, documented, and designed to last. Not just to demo well. I am still building a reputation here on UpWork regardless of my extensive work history, so I am happy to do basic cleaning on datasets or excel sheets as it is something I have done thousands of times. I can typically reduce reporting time by 30% by automating Excel dashboards for weekly sales tracking or other datasets. What makes my work different: I treat documentation and structured context as part of the deliverable, not an afterthought. Every project I ship has a clear paper trail at every stage, so whether it's a teammate, a client, or an AI agent picking it up next, the handoff is smooth and the logic is readable. My current clients are as follows (Part-Time): More Streams: Rebuilding full infrastructure for a music industry platform that was botched by vibe coding. It serves 50+ clients: full-stack campaign tooling, Cloudflare serverless database, Stripe and Spotify API integrations, and 11 -Claude Code automation plugins that eliminated ~40 hours of manual work per week. Wildwood Development: Building predictive models to forecast construction costs and support capital development decisions from quarterly data. Stack: - Infrastructure: Python, SQL, AWS, GCP, Terraform - ML/AI: Scikit-Learn, TensorFlow, PyTorch - Orchestration: Claude Code, multi-agent pipeline design I'm available for contract engagements.

I have B.Sc. Chemistry and Biochemistry from the University of Zimbabwe and M.Sc. Chemistry from the University of Siegen in Germany. I offer consultancy services in chemical synthesis, analysis and research. I hold membership with the American Chemical Society. My working experience is in education, detergents and paint manufacturing industries. I also have non-chemistry skills which include statistical data analysis, freight-forwarding, Excel, Python, Power BI, research proposals, presentations, sales and marketing. I have a total of 16 years working experience. I am also a tutor for university and high school students and subjects taught include maths, physics, chemistry, biology, physical science and combined science. University courses taught include Inorganic Chemistry, Bioinorganic Chemistry, Solid State Chemistry, Organic Chemistry, Biochemistry, Pharmacology, Immunology, Cell and Molecular Biology, Chemical Pathology, Microbiology, Haematology, Genetics and Human Physiology and Disease. I have written textbooks and I write and edit research projects.

As a seasoned Data Scientist, I bring a wealth of experience and expertise to help businesses harness the power of their data. My skills span the entire data science pipeline, from data wrangling and preprocessing to machine learning model development and deployment. I am proficient in programming languages such as Python, R, and SQL, Power BI, Tableau and have hands-on experience with various data manipulation and visualization libraries like Pandas, NumPy, Matplotlib, and Seaborn. I have experience with NLP libraries likes spacy, nlkt, splink(probabilistic record linkage), polyfuzz, hugging face and flair. Also I have experience with LLMs and Generative AI: Vertex AI models Palm and Gemini from Google Cloud. I know how to distill a pretrained Gen AI model to build a chatbot. I have trained AI and ML models in the cloud with MS Azure Databricks and Google Cloud. I have worked for many companies including Hitachi Energy( Fortune 500), Sage Atlanta, Produbanco(Ecuador), SimpleKyc (Australia) and Supercede(UK).