Machine learning, deep learning & MLOps

Predictive models, deep learning, digital twins, forecasting, NLP, model evaluation and MLOps/LLMOps.

53 contributions across 8 experiences

Where this skill was applied

Often together withGenerative AI & LLM solutions Data Strategy for AI Semantic engineering & knowledge management AI governance, risk & regulatory compliance Innovation & change management Solution & enterprise architecture

2019–202413 contributions

DSM-Firmenich

Managed a cross-disciplinary team of data scientists, PhD students, contractors and cloud developers (15+ FTEs), delivering commercial and R&D projects with over €70 million in projected value, recognised by DSM’s scientific community and leadership; nurtured international R&D partnerships with client partners up to C-level.
Coached and led the Digital and Data Science core leadership team (3 FTE) within the broader 15+ FTE organisation — direct reports, team leads, technical leads, academic collaborators and matrixed delivery staff — with clear performance, capability and succession responsibilities.
Founded and led the Digital Hub in Poland, an agile, start-up-like AI research group with proven cost savings of €650,000 per year, and designed its Talent Acquisition Strategy for Digital and Data Science with retention guidelines, cutting AI team turnover by 80%.
Coached regional data scientists and colleagues on data science and digital transformation for projects spanning 60+ R&D labs, 85 manufacturing plants and supply chain, chemistry, compound analytics, formulations, marketing and sales.
Pioneered an AI hybrid digital twin predicting unintended manufacturing-plant shutdowns that reduced downtime by 48% and earned the Best DSM Corporate Research Project award, using deep learning (LSTM, self-attention) on 768 real-time sensor streams and LLMs (GPT-3.5, Llama 2) in LangChain for maintenance-log interpretation.
Implemented a production-grade MLOps framework for AI-driven lab automation (CNN/NLP chromatogram recognition), achieving €1.5M annual ROI and €1.2M annual savings through workflow automation.
Ideated and led research on an AI digital twin modelling the statistical distribution of sun-cream UV filters on skin, aiming to replace in-vivo experiments with digital solutions — a projected value of €30M+ per year.
Coordinated international Corporate Research Program (CRP) activities in Digital Transformation and the Digital Twin Lab — agile pilots for digital disruption, competence development and continuous improvement.
Led R&D on a new data-science model automating the identification and quantification of chemical compounds in chromatograms with neural networks, outperforming lab analysts in accuracy and precision, in partnership with Loughborough University (UK).
Led an information-retrieval project governing millions of lab documents with NLP and transformers (BERT, GPT) for keyword extraction and synonym generation, plus an ontology integrated with public data sources to improve research data interoperability.
Led the development of a machine-learning model to reduce the causes of gas emissions in wastewater treatment plants for vitamins, providing concrete strategies for environmental impact reduction.
Managed collaborations with Contract Research Organisations (CROs) for digital projects, establishing strategic partnerships for external AI/ML development and research.
Speaker at international pharma and health conferences on digital and data management, building DSM’s thought leadership in AI and data science.

2024–202511 contributions

EPAM Systems

Global agribusiness and food company, Switzerland: defined the GenAI strategy for shipping and transport optimisation, structuring 18+ use cases (laytime calculations, contract analysis, chartering risk mitigation) with OCR, NLP, LLMs and knowledge graphs.
Designed the technical questionnaire responses on model orchestration, multi-LLM integration, RAG, embedding strategies and secure deployment, articulating EPAM’s LLMOps approach, AI Factory and compliance measures, and coordinated contributions from global EPAM teams.
Adapted EPAM’s Generative AI Factory target architecture for the bank — three planes (Models / Data / Services) over Data Factory, AI Backoffice and AI Serving Platform — covering feature and prompt engineering, continuous training and evaluation, data- and model-drift monitoring, hallucination management, guardrails, vector stores, FinOps and a gateway to third-party models, closing on a dedicated data-privacy layer.
Global energy leader, Saudi Arabia: designed scalable AWS AI cloud platforms and end-to-end architectures across upstream, downstream, R&D, engineering, finance, HR, safety and supply chain, with LLM orchestration, multi-agent systems, RAG and LSTM models for R&D.
Global industrial materials firm, UK: defined the architecture for freight anomaly detection — hybrid statistical, ML and GenAI models with RAG for contextual validation on AWS and GCP, feature engineering, prompt design for anomaly justification and EPAM AI accelerators.
Leading logistics and postal group, UK: designed a strategic MLOps assessment offering for enterprise-wide AI/ML adoption, with an evaluation methodology across data ops, model management and platform readiness.
Global coffee company, Netherlands: led the analytical assessment of predictive analytics for global shipment delays — historical shipping and customs data, key delay drivers, model performance — and delivered the resulting AI strategy and offering.
Established model-risk controls for predictive and generative AI, including independent validation, bias testing, explainability review, performance thresholds, approval gates, audit trails and periodic revalidation.
Established a production MLOps operating model: experiment tracking, feature management, model registry, reproducible pipelines, CI/CD, automated testing, deployment controls, rollback, monitoring and scheduled retraining.
Defined an LLMOps evaluation framework for RAG and agentic systems measuring retrieval relevance, groundedness, factuality, hallucination rate, task completion, latency, cost per interaction, safety and user satisfaction.
Implemented formal model-lifecycle governance: versioning, approval, retirement, evaluation refresh, data-drift detection and policy updates for predictive models and LLM applications.

20263 contributions

Gavi, the Vaccine Alliance

Designed the Azure architecture extending Gavi’s Programmatic Dashboard from reactive reporting to predictive early warning, forecasting country × vaccine stock-buffer breaches 6–12 months ahead — integrating SAP, UNICEF SD/Thrive360 and VaxNeed into Azure SQL and a row-level-secured Power BI model, with forecasting on Azure Machine Learning and grounded narrative generation on Azure OpenAI behind an API Management LLM gateway.
Skills and technologies: Azure solution and security architecture (App Service, Static Web Apps, Functions, SQL, Blob, Key Vault, API Management, Data Factory, Machine Learning, AI Foundry, AI Search, Monitor) · Snowflake · Azure OpenAI, RAG, multi-agent and agentic AI · MLOps/LLMOps and AI evaluation · AI governance, Responsible AI and EU-aligned AI risk management · Entra ID, RLS, TDE/CMK, PIM, managed identities, TLS · C4 and TOGAF · Power BI, Power Platform, Copilot Studio, Purview, Sentinel · confidential-data governance · innovation portfolio governance, vendor selection and procurement · Design Thinking and rapid prototyping facilitation.
Maintains the Innovation Lab vendor capability matrix, benchmarking six global delivery partners across five domains — AI & Machine Learning, Blockchain & Digital Assets, Immersive & Emerging Tech, Automation & Data, Research & Innovation — as the mandatory input to external-executor selection.

2025–20263 contributions

Solara28

Architected a provider-agnostic LLM service layer (TypeScript/Node.js/Supabase) connecting OpenAI, Anthropic and Gemini APIs, with configurable provider settings, prompt governance, conversation logging and health monitoring, powering conversational and voice-enabled commerce (“Talk to Your Shop”), automated catalogue ingestion from PDFs, photos and Excel, and predictive revenue forecasting.
Embedded operational AI directly into the commerce execution layer: one-click PDF-to-live-store catalogue imports, natural-language conversational assistants, voice-enabled customer ordering via chatbots, a Merchant AI Assistant and predictive CRM analytics built natively into the platform.
Implemented automated loyalty management (points, tiers, rewards) and RFM-driven customer insights integrated with CRM workflows, email marketing automation and customer intelligence to support retention and repeat purchasing.

2012–20133 contributions

United Nations

Led a team of data scientists and architects on a climate-change project, designing a large-scale data architecture managing 1.2 billion records to analyse agrobiodiversity.
Implemented time-series analysis across 3,875 plant sensors in 12 countries for real-time monitoring of climate impact on agricultural systems.
Applied time-series and dimensional modelling, including slowly changing dimensions, to real-time IoT and environmental sensor data.

2002–20113 contributions

United Nations CGIAR

Member of the Academic Committee of the Faculty of Engineering in Information Science at the University of Rome San Paolo, supervising 40+ master’s theses on digital and data science.
Led global initiatives across digital, data science, knowledge management and bioinformatics, progressing from Multimedia Web Specialist to Digital Unit Head with responsibility for strategic planning and implementation.
Built partnerships with universities, vendors and research institutes in data science, digital transformation and business intelligence.

20182 contributions

Fair Trade Handels

Achieved a +561% quarterly revenue increase with a data-analytics marketing campaign using predictive behaviour modelling for the 2018 World Cup.
Head of Data Science and member of the Senior Management Team, leading the shift to a data-driven culture and creating the company’s first Department of Data Science & Reporting.

2013–20182 contributions

Nespresso (Nestlé)

Senior Managing Consultant with broad scope across digital and data science in Nespresso’s commercial and e-commerce business, focused on integration, forecasting, communication and coordination across global markets.
Implemented an A/B/n multivariate testing strategy that significantly improved campaign effectiveness and ROI, integrating sentiment analysis and topic modelling for actionable insights.

Profile8 contributions

Across the career

AI, Data and Technology Executive with 15+ years of international experience in artificial intelligence, Generative AI, machine learning, data strategy and enterprise transformation, combining executive-level AI strategy with hands-on technical leadership and principal engineering experience in AI and Generative AI.
Specialises in Generative AI, LLMs, RAG, Agentic AI, predictive analytics, machine learning, MLOps, LLMOps, AI governance, AI factories and model evaluation; designs enterprise AI and data strategies and defines AI technology stacks and long-term machine-learning roadmaps through hands-on technical leadership.
Combines AI product management, solution architecture, model evaluation, question design and rubric development.
Translates emerging AI capabilities into practical business operations, including document ingestion, segmentation, forecasting, optimisation, personalisation and conversational assistance.
Works across clinical-trial data strategy, TMF metadata, CTMS, eTMF, EDC integration, clinical time-series and regulated data ecosystems, partnering with Contract Research Organisations (CROs) to align clinical-trial data with CTMS, eTMF and EDC systems.
Builds, manages and leads high-performing Swiss-based and international AI, data, architecture, engineering and analytics teams; has built and scaled distributed AI and data organisations spanning data science, machine learning, analytics engineering, cloud, architecture and product roles.
Applies Generative AI to merchant operations, customer engagement, catalogue management, forecasting, personalisation, route optimisation and fraud detection.
Uses predictive RFM segmentation, demand forecasting, personalisation and analytics to support customer retention and commercial performance.

Skills5 contributions

Skills & applied knowledge

AI and Generative-AI technologies: GANs, LLMs, agentic AI, FinOps, Trustworthy AI, MLOps, LLMOps, AI Factory strategy, MCP, A2A, multi-agent systems and Azure AI Foundry.
Mathematics, statistics, experimental design, hypothesis testing and quantitative analysis.
MLOps operating models, deployment practices, monitoring, automation and lifecycle governance.
Artificial intelligence, data mining, neural networks, reinforcement learning, CNNs, LSTMs, attention mechanisms, Bi-GRUs, data engineering and data federation.
Knowledge-management and semantic-AI methods: taxonomy, ontology, graph neural networks, semantic analysis, sentiment analysis and natural-language processing.

All skills

Artificial intelligence

AI governance, risk & regulatory compliance 81 Machine learning, deep learning & MLOps 53 Data Strategy for AI 44 Generative AI & LLM solutions 43 Agentic AI & AI-native engineering 29

Architecture & data platforms

Solution & enterprise architecture 62 Data architecture, engineering & governance 54 Semantic engineering & knowledge management 50 Cloud & multi-tenant SaaS platforms 41

Leadership & business

Innovation & change management 42 Team leadership & talent development 31 Business value & financial management 29 Programme, project & vendor management 27 Executive advisory & pre-sales 19 Entrepreneurship & venture building 13

Product, marketing & communication

Digital marketing & customer analytics 27 Product & full-stack engineering 23 Communication & thought leadership 10