Matteo Castellani

Data Scientist

Innsbruck, Tyrol, Austria12 yrs 8 mos experience

Highly StableAI Enabled

Key Highlights

Expert in building scalable AI systems for product growth.
Proven track record in driving data-driven product decisions.
Strong background in machine learning and data analytics.

Stackforce AI infers this person is a Fintech Data Scientist with expertise in AI-driven product analytics.

Contact

Skills

Core Skills

Machine LearningProduct AnalyticsData AnalysisData ScienceProgramming

Other Skills

Data AnalyticsSQLLooker (Software)Python (Programming Language)Business Intelligence (BI)Google BigQueryTeamworkGoogle Cloud Platform (GCP)Statistical ModelingExperimentation & A/B TestingHypothesis TestingExtract, Transform, Load (ETL)Data EngineeringDashboardsNatural Language Processing (NLP)

About

I am a Senior Data Scientist and Data Analyst with strong experience in applied AI, machine learning, and product-led growth. I work end to end: from data collection and pipeline design to modelling, experimentation, and turning insights into decisions that move core business metrics. In recent years, my focus has increasingly shifted toward Artificial Intelligence systems that scale human work: recommendation engines, intelligent automation, and agentic AI workflows embedded directly into products and operations. My approach is pragmatic and business-oriented: stay data-driven, apply AI where it creates real leverage, and keep a full-system view of the product and organization. I enjoy roles where I can work across product, engineering, and business teams to test ideas quickly and translate AI capabilities into measurable impact on activation, retention, efficiency, and revenue. I have experience across startups and large industrial organizations, building analytics foundations, production ML systems, and AI-driven processes used by thousands of users.

Experience

12 yrs 8 mos

Total Experience

2 yrs 6 mos

Average Tenure

Current Experience

Monkee - saving. rewarded.

Senior Data Scientist / Senior Data Analyst (Growth & Product Analytics)

Jan 2023 – Oct 2025 · 2 yrs 9 mos · Innsbruck, Tyrol, Austria · Hybrid

Monkee helps thousands of users improve their financial health. I worked at the intersection of AI, data science, product analytics, and growth, using machine learning and experimentation to drive product decisions and operational efficiency.
𝗞𝗲𝘆 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗻𝗱 𝗮𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
Built and productionized machine-learning recommendation systems to deliver personalized saving suggestions at scale.
Developed agentic AI systems to automate and triage customer support tickets, significantly reducing manual workload and improving response times.
Designed and trained ML models to classify user spending behavior from raw banking transactions.
Applied AI-driven analysis to identify behavioral patterns, retention drivers, and long-term user value.
Created business and data models for revenue, LTV, and key operational metrics.
Led the company’s growth experimentation process, running data-driven tests to improve activation, retention, and monetization.
Built and maintained end-to-end ETL pipelines and the full data warehouse (BigQuery).
Developed dashboards and reporting used by product, marketing, and leadership teams.
Worked closely with product managers, engineers, and executives to translate AI and data insights into measurable business outcomes.

Data AnalyticsSQLProgrammingData AnalysisMachine LearningLooker (Software)+20

Innio group

DATA ANALYST - INSTALLED BASE

May 2021 – Jan 2023 · 1 yr 8 mos · Innsbruck, Tyrol, Austria

Worked on large-scale industrial datasets supporting INNIO’s global installed base of energy systems. My role combined analytics, data engineering, and early applications of machine learning for data quality and anomaly detection.
𝗞𝗲𝘆 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗻𝗱 𝗮𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
Built and maintained analytics, forecasts, and reporting for the Installed Base database.
Managed and improved the Installed Base layer of INNIO’s enterprise data warehouse.
Applied machine learning and AI-based techniques to detect data inconsistencies, defects, and structural issues across the installed base.
Designed automated data validation and cleaning workflows to improve data reliability at scale.
Delivered dashboards, trend analyses, and insights to internal teams and senior management.
Supported and trained data providers to improve upstream data quality and consistency.