📊 pierdomenico_fiadino.ipynb

A reproducible analysis of one data career — 16 years from a telecom research lab in Vienna to leading data at a European fintech. Everything below is computed from the same source that renders the main site.

You found the hidden notebook edition. The last cell is a live kernel — go on, run it.

In [1]:

import pandas as pd
import matplotlib.pyplot as plt
import piero as pf

print(f"piero v{pf.__version__}  ·  {pf.role} @ {pf.company}")

piero v16.0.0  ·  Senior Director, Head of Data @ Moss

1·Who is this

Pierdomenico Fiadino, PhD — a data leader with a research background, based near Barcelona. He builds and scales data functions that ship data products moving business metrics: product & commercial analytics, analytics engineering, decision science and governance. Currently driving an AI-first transformation of the data org at Moss.

In [2]:

pf.whoami()

Out[2]:

{'name'            : 'Pierdomenico Fiadino, PhD',
 'based_in'        : 'Sabadell, Barcelona · ES',
 'years_experience': 16,
 'years_in_saas'   : 8,
 'publications'    : '30+',
 'phd'             : 'Data Mining & ML — TU Wien (2015)',
 'currently'       : 'AI-first data transformation @ Moss'}

In [3]:

# the whole run, as a DataFrame
pf.jobs

Out[3]:

	company	role	sector	start	end	years
0	Moss	Sr Director, Head of Data	FinTech	2026	—	0.4
1	Docplanner	Global VP of Data	HealthTech	2024	2026	2.0
2	Bird	Head of Data	CPaaS	2023	2024	1.1
3	Bitpanda	Data Analytics Manager	Crypto	2022	2022	1.0
4	Typeform	DS & Analytics Manager	SaaS	2019	2021	2.2
5	Zeelo	Data Lead	Mobility	2018	2019	1.7
6	Eurecat	Senior Data Scientist	R&D	2016	2018	2.1
7	FTW Vienna	R&D Engineer (Data Mining)	Research	2010	2015	5.8

2·Seniority, over time

A monotonic climb: R&D engineer → senior data scientist → lead → manager → head/VP → director. Individual-contributor research in the early 2010s, people- and org-leadership from 2018 on.

In [4]:

ax = pf.trajectory.plot.step(where="post", lw=2.2)
ax.set_yticks(range(1, 7), pf.levels)
ax.set_title("Seniority level over time")

Out[4]:

Text(0.5, 1.0, 'Seniority level over time')

3·Focus areas

Relative emphasis across the toolkit — heavy on analytics and data leadership, with a hands-on analytics-engineering streak and a growing AI/ML weight from the research past.

In [5]:

pf.skills.sort_values().plot.barh(
    color="#1f77b4", title="Focus areas (relative emphasis)")

Out[5]:

<Axes: title={'center': 'Focus areas (relative emphasis)'}>

4·A research past

30+ peer-reviewed publications in networking, data mining and machine learning, clustered around the PhD years at TU Wien and FTW Vienna (2011–2018).

In [6]:

counts = pf.publications.groupby("year").size()
counts.plot.bar(color="#1f77b4", title="Publications per year")
print(f"total: {counts.sum()}+ peer-reviewed publications")

Out[6]:

total: 30+ peer-reviewed publications

5·Education

In [7]:

pf.education

Out[7]:

	degree	field	institution	year
0	PhD	Data Mining & Machine Learning	TU Wien	2015
1	MSc	Computer Engineering	Sapienza, Rome	2010
2	BSc	Computer Engineering	Sapienza, Rome	2008

6·Get in touch

The kernel is still warm. Reach out, grab the CV, or call pf.help() in the cell below.

📄 resume.pdf ✉ p@fiadino.org 💼 LinkedIn 💻 GitHub

In [ ]:

Shift + Enter to run