Ziad Charles Nader
Verified Expert in Engineering
AI Consultant and Developer
Paris, France
Toptal member since February 11, 2026
Ziad is a tech lead in data and machine learning who helps organizations turn ambiguous AI opportunities into scalable, auditable, production-ready systems. He combines hands-on engineering—Python, data pipelines, and ML/LLM workflows—with leadership across architecture, delivery, and team execution. Ziad's approach emphasizes reliability, responsible AI practices, and operational excellence, so models perform consistently. He is especially effective in regulated or complex environments.
Portfolio
Experience
- Business Requirements - 10 years
- Machine Learning - 8 years
- Python - 7 years
- Data Science - 7 years
- Scikit-learn - 5 years
- Natural Language Processing (NLP) - 4 years
- Technical Leadership - 4 years
- Dataiku - 3 years
Preferred Environment
Visual Studio Code (VS Code), GitHub, SQL, Dataiku, Pandas, Scikit-learn, Large Language Models (LLMs), Jira, Supabase
The most amazing...
...personal project I've done is a machine learning model that estimates survival probability for patients receiving a coronary stent following a cardiac arrest.
Work Experience
AI Solution Architect | Tech Lead
Groupe Credit Agricole
- Developed and maintained an agentic framework in the company, making available a set of agents/skills to help teams develop in agentic mode.
- Constructed an MVP with AI-assisted development, putting applications related to financial markets into production.
- Participated in the creation of an AI platform within the enterprise.
Senior AI/ML Engineer
Augura
- Owned end-to-end feature development using Python (FastAPI, SQLAlchemy Async, and Pydantic) with PostgreSQL row-level multi-tenancy, a generated TypeScript client, and a React 19, Vite, and Tailwind workspace.
- Built LLM agents using the Claude API for clinical data profiling and structured extraction, delivering deterministic output, tool-call schemas, and responses constrained to a controlled vocabulary.
- Architected the causal-inference layer in Python and NetworkX, including automated DAG generation, an ontology-backed semantic layer with vector search, confounder, collider, and instrument classification, and an interactive React Flow DAG editor.
- Delivered a machine learning simulation that selected the best causal estimator for each dataset, using scikit-learn ensembles with cross-fitting, NumPy and pandas Monte Carlo generation with known ground truth, coverage and power analysis, and parallelization with joblib.
- Hardened the application against real-world data after production datasets exposed defects that were invisible in synthetic fixtures, resolving them with regression test coverage and implementing quality gates through pytest, Ruff, strict Pyright, and contract drift checks.
- Delivered an agent-first solution with Claude Code, deploying it on Modal (serverless Python) and Vercel.
Tech Lead, Data & AI | Machine Learning Expert
Capgemini
- Architected the global technical roadmap for an AI-based budgeting platform for a leader in the automotive industry, aligning machine learning requirements with scalable architecture and delivery milestones.
- Managed the end-to-end AI budgeting platform for an automotive giant. Used Dataiku to engineer forecasting pipelines and clustering algorithms that segmented financial behaviors, reducing budgeting lead time by around 30%.
- Led the development of a scalable attrition prediction engine for a top HR department using Azure ML. Engineered behavioral features to anticipate workforce risks, successfully deploying the solution across multiple business scopes.
- Directed a POC formaterial slab positioning, employing advanced combinatorial optimization techniques to minimize waste and enhance operational efficiency—a methodology transferable to squad rotation and resource allocation problems.
- Supervised a cross-functional squad of data scientists and data engineers, enforcing best practices in software quality, modularity, and system maintainability.
- Defined the integration and testing strategy, including unit and integration tests for critical financial transformations, to ensure the reliability of the industrial solution.
Senior Data Scientist
Capgemini
- Led a technical POC automating legal text changes for a government agency. Deployed Natural Language Processing (NLP) models to parse documents, accelerating processing speed and saving 40% of agent time.
- Built and deployed a risk-anticipation tool using time-series analysis to detect "weak signals" and anomalies in operational data, enabling proactive intervention before critical failures.
- Delivered a Web Scraping use case to aggregate external data for a fraud detection model, demonstrating strong capabilities in building unconventional data pipelines.
- Implemented a Named Entity Recognition (NER) solution to extract strategic insights from unstructured Request for Proposal (RFP) documents, automating the detection of new business opportunities.
Machine Learning Engineer | Volunteer
Self-employed
- Contributed to "Optimizing Lung Cancer Screening Protocols" (Journal of Thoracic Imaging, 2023), applying statistical rigor to false-positive rate analysis—demonstrating the ability to apply data science to biological/ human performance contexts.
- Collaborated with a cardiologist on predictive modeling of clinical data for early risk detection and outcome classification.
- Explored other medical and AI projects, focusing on the intersection of predictive modeling, data governance, and applied healthcare analytics.
Solution Implementation Lead
Murex
- Steered technical delivery streams for complex risk and reporting systems for major EMEA banking clients, managing the project from initial scoping to final go-live.
- Acted as the primary technical liaison between product owners and developers, translating complex regulatory requirements into scalable architectural features.
- Optimized SQL-based workflows and analyzed high-volume data flows to ensure seamless integration between trading, risk, and finance modules.
Experience
Attrition Prediction Monitoring Tool
Published Paper in Medical Field
“Optimizing Lung Cancer Screening Protocols (Lung-RADS 2.0)”. I contributed to the analysis of false-positive rates and nodule classification
strategies using NLST data.
Football App for Predicting Fantasy Football Recommendations
Education
Master's Degree in Data Science
Paris Dauphine University - Paris, France
Master's Degree in Telecommunications
Telecom Paris - Paris, France
Bachelor's Degree in Electrical and Computer Engineering
American University of Beirut - Beirut
Certifications
Attention Mechanism
Google Cloud
Generative AI with Large Language Models
Coursera
Azure Machine Learning
Microsoft
Skills
Libraries/APIs
Scikit-learn, Pandas, XGBoost, Natural Language Toolkit (NLTK), Beautiful Soup, Node.js, API Development, React
Tools
GitHub, Jira, Tableau, Claude, Microsoft Power BI, Murex, Claude Code, GitHub Copilot
Languages
Python, SQL, R
Platforms
Jupyter Notebook, Dataiku, Visual Studio Code (VS Code), Azure, Google Cloud Platform (GCP), Amazon Web Services (AWS)
Storage
Google Cloud
Industry Expertise
Healthcare
Other
Machine Learning, Business Requirements, Natural Language Processing (NLP), Large Language Models (LLMs), Data Science, Artificial Intelligence (AI), Data Analysis, Predictive Modeling, Data Scientist, Technical Leadership, IT Management, IT Projects, Retrieval-augmented Generation (RAG), LangChain, Machine Learning Operations (MLOps), Dashboards, Financial Data, Statistics, Analysis of Variance (ANOVA), Model Validation, Time Series Analysis, Regression Modeling, Model Evaluation, Predictive Analytics, Forecasting, Data Analytics, Risk Modeling, Website Data Scraping, Anthropic, Workflow Automation, Agentic AI Systems, Architecture, LLM Integration, Churn Analysis, Time Series, Clustering, Statistical Methods, Data-informed Recommendations, 3G, Digital Communication, Information Theory, Algorithms, Mathematics, Electrical Engineering, Agentic AI, Generative Artificial Intelligence (GenAI), Data Engineering, APIs, Generative Pre-trained Transformers (GPT), OpenAI, Biostatistics, AI Agents, AI Pipeline, Supabase, AI Architecture, Optical Character Recognition (OCR), LLM Agents, Amazon Bedrock AgentCore, Engines, Front-end Development
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