Role Description - Analyse et exploitation des données : - Analyse et adaptez les requêtes nationales pour leur mise en œuvre opérationnelle. - Réalisez des requêtes ciblées sur les bases de données de l'assurance maladie. - Mettez en forme les résultats et évaluez leur pertinence et efficacité. - Participez à la construction du plan de contrôle et à la priorisation des dossiers à investiguer. - Réalisez des études et ciblages par datamining pour identifier les situations à risque. - Pilotage des projets et optimisation des dispositifs de lutte contre la fraude : - Pilotez et conduisez des projets relatifs à la lutte contre les fraudes à la facturation. - Assurez une veille stratégique, technique et réglementaire. - Contribuez à l'amélioration des dispositifs de détection et de contrôle. - Gestion des outils, des indicateurs et des procédures : - Recensez les besoins d'évolution des outils de gestion. - Rédigez les expressions de besoin et assurez le suivi de leur réalisation. - Suivez les indicateurs d'activité et concevez les tableaux de bord nécessaires. - Participez à l'élaboration du bilan annuel et du plan d'actions de lutte contre la fraude. - Rédigez, mettez à jour et publiez les procédures dans les référentiels dédiés. - Représentation institutionnelle et coordination : - Représentez la CNMSS au sein des instances de pilotage et des groupes de travail. - Assurez le suivi des signalements transmis par les partenaires externes. - Contribuez au développement des échanges avec les organismes d'assurance maladie. - Activités complémentaires : - Participez aux contrôles internes conduits par le service. - Contribuez à l'identification des besoins de formation des agents. - Participez à l'alimentation de la revue de chaîne du processus « Traitement des abus et des fraudes ». - Réalisez des requêtes sur les bases de données de l'assurance maladie. Qualifications - Titulaire d'un diplôme de niveau Bac +2 à Bac +3 ou équivalent. - Idéalement dans les domaines de la data, des statistiques, de l'informatique décisionnelle, de la protection sociale ou de la gestion des risques. - Maîtrise des outils de datamining et d'analyse de données (SQL, Excel avancé, Power BI, Tableau). - Connaissance de la réglementation relative à la lutte contre les fraudes et du droit de l'assurance maladie. - Capacité à analyser les besoins et l'existant, conduire des projets, organiser son activité. - Solides capacités d'analyse, travail en équipe, autonomie, réactivité, curiosité et esprit d'initiative. - Une expérience significative en analyse de données, datamining, gestion de projet et/ou en lutte contre les fraudes est appréciée. Requirements - Localisation : 247 Av Jacques CARTIER 83090 TOULON CEDEX 9 Elements de candidature - Documents à transmettre : CV et lettre de motivation obligatoires.

Role Description Work in conjunction with DCoE team to deliver: - Additional stored procs OR notebook to build these extra 230 data items into new DCoE data tables/views - An extensible process where newly required data fields could be easily added into config with minimal re-engineering effort - Self-documenting code with decent commentary and structure - Responsibility for other deliverables including business analysis, documentation, design sign-offs, data model and testing will be covered by DCoE Company Description At Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. - At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus. - Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. - Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself. - We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. - We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. - All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.

Role Description We are seeking a self-directed, hands-on Data Engineer to build the data foundation that powers the entire organization. Today, our data lives across 250+ transactional tables refreshed weekly. You will own consolidating this into ~10 well-defined analytic tables, transitioning from full refreshes to daily (or better) incremental loads, and standing up a centralized analytics layer that BI, product, and AI can draw from with confidence. - Own data pipeline design and implementation; consolidate 250+ transactional tables into ~10 analytic tables; move from weekly full-refresh to daily/near-real-time incremental loads via CDC (e.g., AWS DMS). - Build and own the centralized analytics layer. - Establish unified data definitions, metrics, KPIs, and benchmarks across the organization. - Deliver a Master Data Management (MDM) framework with unified definitions and governance standards. - Drive migration to the centralized layer while deprecating direct raw table access. - Partner with internal departments to drive self-service analytics and build aggregations/data marts for benchmarking and experimentation. - Conduct exploratory and statistical analysis for data validation, and implement data observability/monitoring. - Partner with the Applied AI Engineer to design AI-ready data models (including retrieval- and embedding-ready structures to ground RAG and LLM applications). - Expand the data lake to ingest third-party sources (e.g., HubSpot, Jira, Zendesk). Qualifications - 5+ years of data engineering experience building/operating production pipelines and analytics layers. - Strong SQL and Python, with deep hands-on PostgreSQL expertise. - Deep experience in dimensional modeling and consolidating transactional schemas into analytic tables. - Hands-on experience with Change Data Capture (CDC) and incremental loading strategies. - Hands-on experience with the AWS Data Stack (Athena, Glue, DMS, S3, RDS, Lambda). - Proven experience establishing Master Data Management (MDM), metrics definitions, and data governance. - Hands-on experience constructing AI-ready pipelines (retrieval/embedding-ready data for RAG/LLM applications). Requirements - Experience integrating SaaS third-party tools (HubSpot, Jira, Zendesk) into data lakes/warehouses. - Experience partnering with AI/ML teams on feature stores and ML evaluation pipelines. - Familiarity with data observability and quality monitoring tooling. - Background in multi-tenant SaaS, edtech, or compliance platforms. Benefits - Trust-based culture: We highly value ownership and we trust in our team's skills and seniority to achieve goals. - Remote work: Enjoy working remotely! We are spread-headed in different locations in LATAM and Europe. - Flexible time off policy. - Diverse & Impactful Projects: Engage with complex challenges for major players in Media & Entertainment, alongside innovative startups in Solar Energy, Compliance, and other dynamic sectors. - Learning and development: Get the opportunity to take English classes, trainings and certifications. - Referral Program: We value connection! Our referral program provides bonuses for successful referrals. - Strong culture and traditions: We foster a supportive and engaging work environment, highlighted by our annual “Binagora Week”, regular team gatherings and gifts for special occasions.

• Establish engagement plan, governance cadence, interview schedule, and workstream structure • Confirm in-scope verticals, stakeholder groups, and required artefacts • Conduct stakeholder interviews and working sessions across pillars and functions • Document current-state operating model, processes, decision points, and friction points • Identify AI-enabled use cases across processes, workflows, and engineering delivery for each in-scope pillar • Define opportunities for pillar-specific agents and a cross-pillar orchestration agent • Populate and maintain the AI Initiatives Tracker (Innovation Backlog, AI Initiatives Register, Risk & Controls, Benefits Tracking) • Map current or recommended AI tooling (e.g., M365 Copilot, GitHub Copilot, Claude Code, ChatGPT) to each use case • Assess technical feasibility, control requirements, and data-handling implications for prioritized use cases • Define target-state operating model, human-in-the-loop points, and governance forums • Prioritize opportunities and build a phased implementation roadmap (near/medium/long-term) • Prepare executive-ready findings report and leadership presentation • Lead validation workshops and readout/knowledge-transfer sessions with client stakeholders