Nicolas Fernandez
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Solutions Engineer @ ❄️
Multiple roles related to data & analytics. As a data scientist consultant, I’ve built ML solutions mainly for Financial Services and Retail for a wide range of applications; churn prediction, stock replacement and discount optimization, anomaly detection, interactive data visualization applications. In banking, I have been involved in the model risk management of ML/AI models. I specialized in topics such as model explainability, fairness, monitoring and KYC. My current role in tech combines the understanding of building data solutions, quantification of the value they can provide, and how these are integrated and managed by different stakeholders in an organization.
Universitat Politècnica de Catalunya
Industrial Engineering - 1st year
N/A – Present
University of Valencia
MSc Applied Artificial Intelligence
N/A – Present
Universitat Autònoma de Barcelona
BSc Applied Statistics
N/A – Present
Snowflake
Solutions Engineer
September 1, 2023 – Present
Амстердам
Alteryx
Solutions Engineer
October 1, 2022 – September 1, 2023
Países Bajos
DataRobot
Solutions Engineer
September 1, 2021 – October 1, 2022
Netherlands
ING
Data Scientist - Model Risk Management
February 1, 2020 – September 1, 2021
Países Bajos · On-site
Deloitte Digital
Data Scientist
December 1, 2018 – January 1, 2020
Madrid Area, Spain
EY
Data Scientist
January 1, 2017 – December 1, 2018
Madrid Area, Spain
Institut de Recerca Biomèdica de Lleida Fundació Dr. Pifarré - IRBLLEIDA
Research Intern
January 1, 2016 – December 1, 2016
Lleida, España
Banco Sabadell
Developer Internship
June 1, 2015 – August 1, 2015
Andorra
SnowPro Core Certification
Snowflake
June 24, 2026 – Present
Alteryx Designer Advanced
Alteryx
June 24, 2026 – Present
Master Data Science (Data Science Foundations, Advanced ML, Data lifecycle, Python)
Universitat Oberta de Catalunya
June 24, 2026 – Present
MLOps III
DataRobot
June 24, 2026 – Present
Machine Learning Framework for Data Scientists
DataRobot
June 24, 2026 – Present
Nova Member
Nova
June 24, 2026 – Present
Banking Fundamentals diploma
NIBE-SVV Opleidingen
June 24, 2026 – Present
Google Cloud Platform: Core Infrastructure
Coursera
June 24, 2026 – Present
Statistics in Medicine
Stanford University
June 24, 2026 – Present
Foundations of Financial Risks
GARP FRM Program
June 24, 2026 – Present
Statistical Learning
Stanford University
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong background in data science and analytics, working across various sectors including finance, retail, and healthcare, and with different types of organizations (consulting, product companies, banking, research). This diversity of experience suggests adaptability and a broad understanding of different business contexts. Their roles as Solutions Engineer indicate a customer-centric and problem-solving mindset, which aligns well with roles requiring interaction with stakeholders and understanding business needs. The transition from hands-on data scientist to solutions engineer might indicate a preference for client engagement and product application over deep technical development, which should be considered for a pure Data Analyst role. The target role of 'Data Analyst' might be a step back from their 'Data Scientist' and 'Solutions Engineer' roles, which typically involve more advanced modeling and strategic responsibilities. However, their foundational skills are highly relevant.
Soft Skills & Operational Fit
The candidate's experience as a Solutions Engineer at Snowflake, Alteryx, and DataRobot suggests strong client-facing communication, problem-solving, and presentation skills. Their role in Model Risk Management at ING indicates an understanding of governance, compliance, and critical evaluation of data science initiatives. The diverse project experience implies adaptability and a collaborative approach.