DGFreelancer profile translated to English.
Back to original languageAbout Daniela
Do you have data, but lack the clarity to make decisions, prioritize, or drive performance?
I help companies leverage their data to transform it into clear, actionable, and reliable indicators.
Data Analyst / Data Scientistwith8 years of experiencein demanding environments (particularly banking and insurance), I work across the entire data value chain:
from understanding business needs to deploying decision-support tools.
What I bring you concretely
- Business-oriented analysis, not disconnected tables of numbers
- Clear and automated dashboards to track your KPIs in real-time
- In-depth analyses to understand your customers, your performance, or your anomalies
- Robust, documented, and maintainable solutions, adapted to your teams
My added value
- Ability to bridge the gap between data, business objectives, and decision-making
- Strong analytical rigor (data quality, reliability of results)
- Experience in complex and regulated environments
- Autonomy, pedagogical approach, and ability to simplify technical subjects
Types of projects and deliverables I produce
- Exploratory analyses and ad hoc studies
- Dashboards (Power BI / BI tools)
- Performance modeling and indicators
- Customer, marketing, or financial analyses
- Automation of reporting and data pipelines
- Documentation and clear reporting to business teams
- Descriptive and predictive data analysis using advanced Machine Learning and Deep Learning techniques to identify trends, anticipate behaviors, and support decision-making.
My goal:to save you time, provide visibility, and build confidence in your decisions through data.
English
Native or bilingual
French
Native or bilingual
Spanish
Native or bilingual
Can work on-site
Bordeaux (up to 50km)
Experience
- Crédit AgricoleData AnalystBANKING AND INSURANCEJuly 2025 - December 2025 (5 months)Bordeaux, FranceProject:Marketing and relationship data analysis, optimization of commercial campaigns, design of MicroStrategy dashboards, improvement of steering indicators, and contribution to CRM and digital analyses for the bank.Missions:Marketing & CRM Analysis● Analysis of leads, relationships, and digital data to identify commercial opportunities.● Statistical studies on customer behavior and relationship journeys.● Monitoring of marketing campaign performance and operational recommendations.Dashboard Design● Creation and management of dashboards using MicroStrategy.● Data quality improvement and validation for reporting.In-depth Analyses & Decision Support● Ad hoc analyses for business units: customer segmentation, relationship intensity, conversion funnel KPIs.● Development of relevant indicators to improve CRM strategies.● Synthesis of results and recommendations for operational teams.Collaboration with Business Teams● Gathering requirements and translating them into data analysis.● Presenting analyses, insights, and conclusions to managers and marketing leads.● Supporting teams in interpreting indicators and implementing actions.Tools & Technical Environment:● Languages: SAS, SQL● BI Tools: MicroStrategy● Databases: SAS● Cloud: Hive (integrated into Zeus, Crédit Agricole's internal data ecosystem)Domain: Banking / Marketing / CRM
- Caisse d'Épargne Aquitaine Poitou-CharantesData ScientistBANKING AND INSURANCEDecember 2024 - June 2025 (6 months)Bordeaux, FranceProject:Implementation of time series forecasting projects applied to mortgage sales and treasury management, acting as an interface between business units (mortgage and ALM) and technical teams. Role: Data ScientistMissions:Requirements Gathering and Business Interface● Regular communication with mortgage and ALM departments to understand their challenges, expectations, and constraints.● Translating business needs into data problems and defining appropriate deliverables.● Presenting results to both business and technical teams.Data Science Projects – Time Series● Mortgage sales forecasting:- Objective: predict the daily number of mortgage loans taken out by individuals.● Treasury forecasting:- Objective: estimate daily treasury amount in an ALM (Asset and Liability Management) context.● Methodology applied:- Data collection, exploratory analysis, pre-processing.- Selection of explanatory variables.- Modeling, prediction, performance evaluation using appropriate metrics.- Presentation of results to stakeholders.● Models explored:- Statistical models: ARIMA, SARIMA, SARIMAX.- Machine learning models: XGBoost, LightGBM, Random Forest, Prophet- Neural networks (LSTM, GRU, etc.).- Pre-trained models: MOIRAI● Libraries tested:- Darts, Sktime, Etna, StatsForecast, Moirai (Salesforce).Tools & Technical Environment:● Languages: Python● IDE: Visual Studio Code● Cloud: Google Cloud Platform● Collaboration: Azure DevOps, Bitbucket, Confluence OS: Windows, GCP cloud environmentDomain:Banking – Mortgage & Treasury (ALM)
- Groupama Gan VieData Scientist / Data AnalystBANKING AND INSURANCEJune 2023 - November 2024 (1 year and 5 months)Mérignac, FranceProject:Design and deployment of solutions for verbatim analysis, fraud detection, and churn prediction, as well as the creation of decision-support tools, with a role of close advisor and data science trainer.Missions:Data Science Projects● Verbatim Analysis:Project carried out with Python to predict customer satisfaction or dissatisfaction and identify topics discussed in comments.● Fraud Detection:Development of a fraud detection project for sick leave claims, based on a database of fraudulent customers and customer history.● Churn Prediction:Development of a churn prediction score for a set of products.● Creation of a project to call customers who have churned to retain them.Decision Support Tools● Design and development of a decision-support tool for claims management departments, simplifying their daily work.- Technologies Used: SAS for database exploitation, MySQL for application creation.Missions as a Close Advisor● Inventory of all management dashboards to centralize this data.● Participation and presentation of projects in a group data club.● Leading a data club within the department, where each service presents its data topics.Training● Mentoring:Supervised four interns, three of whom were hired on permanent contracts.● Training:Trained approximately ten people in SAS, Qlik Sense, and company databases.Technical Environment:● Languages: Python, SQL● API and Frameworks: Keras, Scikit-learn● Application Server: SAS, MySQL● Platform on UNIX, Windows environments● Tools: Visual Studio Code, SAS Enterprise Guide, Qlik Sense, Git, Jira
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Education
- Data Scientist (continuing education - 9 months)Mines ParisTech2023La formation Data Scientist de DataScientest en collaboration avec Mines Paris – PSL Executive Education est un parcours certifiant de niveau Bac + 5 (RNCP niveau 7) conçu pour former des professionnels capables de maîtriser l’ensemble des étapes de la science des données, de l’exploration à la mise en production de solutions data.
- Master 2 in Money, Finance, Banking, InsuranceLe Mans Université2018Le Master 2 MBFA vise à former des spécialistes dans les domaines de la banque, de la finance et de l’assurance, capables d’analyser les marchés, d’évaluer les risques financiers et assurantiels, et de proposer des solutions adaptées aux besoins des entreprises et des clients. Il combine des connaissances théoriques approfondies et des compétences professionnelles opérationnelles.