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Nauval Z
@nauvalzulfikar
RAG chatbots and AI agents for teams where a wrong answer is expensive
Japón
Alemán, Indonesio, Inglés, Japonés
Sobre mí
I build RAG chatbots and AI agents for companies where a wrong answer is expensive: banks, government, regulated industry.
5+ years in data science. Lead Data Scientist at an OJK-regulated bank (credit-risk NLP, +5.3% portfolio return). Data automation for a government agency (45% faster, 80% less manual work). MSc Business Analytics, Aston University UK, First Class, full scholarship. Two peer-reviewed papers.
Python, LangChain, AWS, Docker, PySpark. Every build ships with an eval set, citations and handover docs, not a demo.
Send 3 sample docs and I'll tell you if RAG is the right answer.
Habilidades
Nauval Z
desconectado •
Revisa mis servicios
Desarrollo de chatbots de IA
I will build ai chatbot, rag chatbot, langchain chatbot over your documents
Integraciones de IA
I will build ai agent, ai automation, n8n workflow automation for your reports
Experiencia laboral
Data Scientist & Project Manager — Dinas PUTR Kab. Bandung
Bandung • Tiempo completo
Sep 2022 - Present • 4 yrs 1 mo
Automated ETL pipelines and Power BI dashboards that replaced manual data entry across multiple bureaus. Embedded model governance and audit trails into government data workflows, so every number in a report can be traced back to its source. Delivered 45% faster project delivery and a 20% gain in data efficiency. Led cross-functional Agile teams in Jira. Stack: Python, PySpark, AWS, Power BI.
Marketing Data Scientist (Internship)
SyncWell - Know Yourself
Jul 2024 - Sep 2024 • 2 mos
Built predictive ML models for customer engagement and campaign targeting, so marketing spend went to the segments most likely to convert. Designed and ran A/B tests and implemented automated campaign trigger logic. Achieved +40% CTR and +22% follower growth on owned channels. Stack: Python, SQL, Tableau, Salesforce.
Strategic Data Scientist Specialist (Internship)
PCOS Challenge
Jun 2024 - Aug 2024 • 2 mos
Developed a customer attribution model linking donor touchpoints to conversions, so the team could see which channels actually drove giving. Automated the report-extraction pipeline and cut manual workload by 80%. Stack: Python, Streamlit, Power BI, PySpark, Hadoop.
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