andry michael Barbaza

Conducted an end-to-end profitability analysis to assess business performance across projects, activities and organisational structures in a multi-site environment with fragmented financial data. – Extracted, consolidated, cleaned and validated financial and operational data from 100+ employees across multiple source systems, building a unified dataset for financial performance reporting: data cleaning, follow-ups, management of atypical cases – Modelled the cost allocation logic in cascade (projects → activities → structures) according to business rules – Reconciled heterogeneous financial sources (General Ledger, payroll, expenses, suppliers, vehicles); calculated TVI rate I – Developed Power BI dashboards (Import – SQL Server) with M and DAX transformations; Performed ad hoc profitability analyses to support management decision-making – Supported IT in deploying an on-premise Power BI Report Server connected to a SQL Server data lake

Data vizualisation Specialist & Power BI Champion - Conduct workshops with business to align needs and go/no go criteria - Liquidity monitoring dashboarg implementation - Lead UAT and quality assurance activities, document knowledge transfer - Conduct change management by providing training on how to use the tool via Power BI.and streamlit (python) At Crédit Agricole CIB – Risk Department: Context: within the RISK IT department, support for regulatory and internal risk monitoring in a large banking institution. Job's purpose: design and industrialize monitoring dashboards for liquidity risk, specifically LCR (Liquidity Coverage Ratio) indicators, to assist regulatory reporting and strategic decision-making. How: worked in a cross-functional team (IT + Risk Analysts) to model LCR flows and inflows/outflows based on a complex financial database (Vertica). Developed M queries and measures in Power BI to calculate accurate liquidity indicators. Developed robust dashboards in DirectQuery mode to ensure real-time data access and performance optimization. Conducted data transformations and logic modeling (flags, weights, eligibility, etc.) using Power Query / M and advanced DAX. Participated in Agile ceremonies, interfaced with business stakeholders to refine needs, and ensured continuity of deliverables. Tools: Power BI, Vertica SQL, Power Query M, Excel, Git, streamlit Methodology: Agile / Scrum.

At Servier's Finance Factory: - Context: procure-to-pay process involves the whole purchasing cycle, from identifying needs, selecting vendors, keeping compliance, receiving, and paying. - Job's purpose: optimize processes and mitigate risks: payment terms, PO coverage, and contract terms. Support in areas such as the dematerialization of invoices (legal compliance), adoption of a "full suit" tool that would bring together all current tools (SAP, Corcentric, Aravo, etc.) in the cloud. - How: Codevelopment with the purchasing finance feature team on reporting solutions. Development of solutions in agile mode, as business Product Owner on one side and data analyst on the other. - Transformation of data from ERP systems (SAP, JDE), automated via Dbt and Argo - Workshop and needs assessment to create relevant Power BI dashboards - Run management using SSIS / SSAS tools - Agile methods: Scrum

Leading Smartphones & IoT market and business analysis, tracking internal and external devices sales performance, assessing supplier’s and product’s dynamics, identifying key market trends and opportunities, and providing strategic steering guidance to Orange Group and Affiliates.

Context : With less staff, Airbus has an imperative need for digitalisation and automation in its design offices. It will have to ensure the progressive increase in production rate and the problematic of my work then became to answer the needs of digitalization of the engineers thanks to the data. Project : Digitalisation support in the A320 Structure design office, composed of 3 departments of about 30 engineers each. Assistant data scientist : - Collection and study of business needs - Collection of needs (creation of user stories and distribution to the department) - Automation of tasks (Python, scripting) and coding of industrial programs - Creation of Cockpits with standardised SQL queries, checking data quality, searching for deltas in the various databases - Champion of the PETSuit project (machine learning on aircraft derogations) - Key points: I was part of the cross-functional team in the design office. Correspondence was in English even though official documents were translated into several languages. Industrial context so no data visualisation - Adaptation was the key word of my stay at Airbus: new work context, new tools (VBA) to learn, health crisis management. - Proposal of functional solutions - Internal communication on the new processes - Creation and execution with the development team of optimisation and development plans - Collaboration with technical teams Technical environment : Google workspace, Canva, Python automation (jupyter), scripting, dashboard fed by SQL queries. Functional environment : Process automation, functional support, writing functional specifications / user stories / requirements, training Languages: French, English Methodology: Agile SCRUM

Open Data Consultant My recruitment was precipitated by a legal obligation related to the availability of public digital data which took effect on 7 October 2018 (Lemaire law): Open Data by default. My mission consisted in setting up an Open Data strategy and implementing it in the Nancy Ville Numérique (NVN) department and the municipal archives department. - Monitoring of Open Data achievements in local authorities (hosting, resources, etc.) - Training for the sharing of good practices with agents, presentation of the strategy to the various parties (metropolises, Open Data actors, etc.) - Census, collection, cleaning, processing and sharing of essential data (NLP LER processing for some data available in pdf) - Consultant on DSI projects from a Data Science point of view Stat consultant The NVN department is in charge of the 69th of the 90 "actions for immediate action" proposed by the city: the creation of an interface allowing Nancy residents to be better informed about their energy consumption (particularly electricity) in order to guide them towards more eco-citizen behaviour. Project : Research into a statistical estimation model of a household's electricity consumption through an interface that would collect a few simple questions. - Preliminary study: research into statistical variables and monitoring of DPE (Diagnosis of Energy Performance) calculation studies - Technical and functional description of the final tool integrating the model and proposal of a model - Multiple Component Analysis with data collected in Open Data - Contact with stakeholders (local authorities, ADEME, electricity suppliers) Technical environment : Confluence Attlassion, Python processing: Panda, Numpy, Scikit-learn, Statsmodels, Seaborn, GATE, NLTK, R processing: dplyr, ggplot2, Rmardown, shiny, leaflet, Factominer, Canva , Trello, Projectlibre

Background: The objective of our (5 students) supervised research was to use learning and unsupervised algorithms to obtain a satisfactory model to identify supernovas from light curves. An interface will present the selected model and the different clusters. This was a commission from the LIRMM and the Marseille Astrophysics Laboratory. Project : Visualisation and analysis of light curves - State of the art of the techniques used, data collection with sql queries via Kaggle - clustering of metrics (PCA and t-SNE) with KMeans - Visualisation of light curves (time series, sparse data, Mollweid) - Feature extraction in deep learning (CNN encoder) and light curve reconstruction (CNN decoder) Technical environment : Stat: RStudio / Machine learning and Deep learning: Python / SQLServer / Interface: Python Dash (chart.js) / Google drive / ProjetLibre