Main coursework: Machine Learning · Econometrics · Applied Maths & Statistics · Causal Inference · NLP · Deep Learning · Time‑Series Analysis
class Viktoria:
def __init__(self):
self.name = "Viktoria Gagua"
self.education = [
"MSc Data Science, Barcelona School of Economics (GPA 8.51/10)",
"BA Social Science—Economics & Finance, Free University of Tbilisi (GPA 3.8/4)"
]
self.interests = [
"Data Science",
"Fintech",
"Machine Learning",
"Time‑Series Forecasting",
"Causal Inference",
"Deep Learning"
]
self.languages = {"Georgian": "native", "English": "fluent", "Spanish": "basic"}
self.tools = [
"Python", "SQL", "R", "Power BI + DAX",
"PostgreSQL", "MS SQL Server", "Oracle",
"Git / GitHub"
]
def previous_focus(self):
return (
"Machine Learning and NLP projects at BSE\n"
"Consulting on merchant analytics in fintech"
)-
🏦 TBI Bank (Consultant) — Built an interactive merchant‑KPIs analytics suite and an RFM‑based early‑warning system that re‑activated 30 % of dormant merchants in 3 months.
-
📈 Bank of Georgia — Used SQL‑driven audience segmentation and A/B testing to lift marketing‑campaign ROI; mentored a junior analyst on workflows and data culture.
-
📊 TBC Insurance — Developed Power BI dashboards covering corporate, retail, and SME channels, automated monthly sales reports, and provided industry‑trend analyses for leadership.
-
🛠️ EPAM Systems — Data Engineering Lab — Designed relational schemas, ETL pipelines, and a cloud data‑warehouse prototype, gaining end‑to‑end data‑management experience.
DiD + NLP quantify the surge in Barcelona hotel rates triggered by F1 race weekends.
TF‑IDF mining of UNGA speeches tracks Sustainable Development Goal discourse over decades.
BERT vs. rule‑based baselines for online‑toxicity detection, with augmentation & model distillation.
