Manufacturing, chemicals & industrials

Manufacturing plants, chemicals, industrial materials and lab automation.

11 contributions across 2 experiences

Where this industry appears in the career

2019–20246 contributions

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.
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 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.
Designed the data governance strategy for R&D and manufacturing on the DAMA Body of Knowledge, with policies, FAIRness guidelines, quality standards and data stewardship aligned to the corporate digital strategy.

2024–20253 contributions

Spearheaded the global expansion of EPAM’s Data & AI practice across EMEA, North America, APAC, LATAM and CIS, defining strategic direction for AI, Generative AI and Agentic AI, and led client engagements and pre-sales across banking, government services, logistics, consumer goods, biopharma, chemicals, telecommunications, energy and industrials.
Global chemical manufacturer: lead architect for GenAI in R&D, engineering and production — semantic ingestion pipelines, RAG over structured and unstructured data, and a phased path from PoCs to multi-agent assistants on a modular, scalable architecture.
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.

Profile2 contributions

Brings cross-industry experience spanning biopharma, banking, insurance, life sciences, chemicals, manufacturing, supply chain, finance, e-commerce, public sector, NGOs and FMCG.
Has delivered AI and data solutions for both scientific and commercial environments, from biomedical R&D and manufacturing to retail and local commerce.

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