i
ivan_jcs

Ivan C

@ivan_jcs

Data Analyst, BI Developer

Argentina
Inglês, Espanhol
Algumas informações são exibidas no idioma inglês.
Sobre mim
Stop drowning in raw data. I am a Data Analyst & BI Developer who turns complex datasets into profitable decisions. Backed by a Systems Engineering background, I build automated, executive Power BI dashboards that eliminate cognitive load. My goal? A 5-second health check of your business. Expertise: Power BI, SQL, Python. Focus: SaaS profitability, E-commerce, Marketing. Let's stop guessing and start optimizing. Message me to discuss your data!... Saiba mais

Habilidades

i
ivan_jcs
Ivan C
offline • 
Tempo médio de resposta: 1 hora

Conheça meus serviços

BI Analytics
I will do a strategic b2b saas power bi dashboard
A partir deUS$ 30 /projetoMais detalhes
Análise de Marketing
I will develop a tactical marketing and ad spend power bi dashboard
A partir deUS$ 30 /projetoMais detalhes

Portfólio

Experiência profissional

Fiverr

Freelance Data Analyst & BI Developer

Fiverr • Freelance

Dec 2024 - Present • 1 yr 6 mos

Delivering end to end data analytics and business intelligence solutions to optimize decision-making, reduce costs, and maximize ROI for diverse business models. Key Projects & Achievements: • B2B SaaS Profitability Analytics: Engineered a complete BI solution in Power BI using Star Schema and advanced DAX to diagnose margin erosion. Applied cognitive UX design principles to logically group financial KPIs, significantly accelerating executive time to insight. • Tactical Marketing & E-commerce Dashboard: Developed a visual decision-making tool to track the Customer Journey, CAC vs. LTV, and campaign performance. Engineered the data model to isolate high-spend/low-conversion anomalies, allowing immediate optimization of ad budgets. • AI-Driven Supply Chain Optimization: Designed a discrete-event simulator in Python. Trained a Random Forest Regressor with time-series feature engineering (achieving a 5% error margin) to validate a dynamic inventory replenishment strategy, projecting a theoretical 73% reduction in operational costs.