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The "Data Scientist" is the Big Data specialist in companies, responsible for structuring information and optimizing the security of stored data, regardless of their volume. Their mission: carefully categorize data to avoid risks of IT system failure. They thus seek to detect new vulnerabilities potentially exploitable by hackers and their impact on company activities. Ultimately, they ensure to propose effective protection solutions. This expert in massive data management and analysis is simultaneously a specialist in numbers, statistics, and computer programs: they extract value from data to support the company in making strategic or operational decisions. The "Data Scientist" collaborates transversally with various profiles: computer scientists, statisticians, data analysts, data miners, marketing and web marketing experts...

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Contractor job100960/Product Owner technique Socle IA et Agentique (IA Gen) de la Plateforme Data & IA - Nantes

WorldWide People
Published on
AI
12 months
400 €
Nantes, Pays de la Loire
Product Owner technique sur le périmètre Socle IA et Agentique (IA Gen) de la Plateforme Data & IA. Nantes L'objectif est de structurer et d'industrialiser les capacités plateforme qui permettent : - Côté Socle IA, de fabriquer, déployer et opérer des modèles d'IA classique (cycle de vie ML de bout en bout) ; - Côté IA Gen, d'outiller le passage à l'échelle de l'agentique (outils, frameworks, patterns) et de sécuriser le cycle de vie global d'un produit agentique (de la conception à l'exploitation). Les services LLM et les services partagés (OCR, Embedder, Speech2Text) étant déjà pilotés par des PO, l'enjeu de cette mission est de compléter le dispositif sur la brique agentique et de garantir une industrialisation robuste : réutilisabilité, qualité de service, observabilité, gouvernance d'usage et adoption. Cadrage & priorisation (avec le PM) - Qualifier les besoins, formaliser les cas d'usage et critères d'acceptation. - Décliner la vision PM en backlog/roadmap opérationnels, arbitrer valeur/risque/effort. Socle IA – cycle de vie ML - Piloter les évolutions plateforme permettant la fabrication et le déploiement : industrialisation des parcours, standards, documentation, templates - Contribuer aux exigences run : observabilité, performance, fiabilité, exploitabilité. IA Gen – outillage agentique - Définir et piloter la mise en place des outils & frameworks (orchestration, composants réutilisables, patterns). - Structurer le cycle de vie agentique : intégration, tests, évaluation, supervision, sécurité/guardrails, itérations. Pilotage delivery - Animer le backlog (refinement, planning, démos), suivre l'avancement, gérer dépendances/risques. - Coordonner dev/lead tech/data scientists/ops/archi, et sécuriser la qualité des livraisons. Adoption & documentation - Produire/mettre à jour la doc produit (guides, exemples, bonnes pratiques) et accompagner les équipes dans la prise en main. - Mettre en place des KPIs (usage, satisfaction, incidents, coût) et alimenter l'amélioration continue.
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Frequently asked questions about working as a Data Scientist

What is the role of a Data Scientist

In the Big Data era, the Data Scientist is somewhat the evolution of the Data Analyst. He/she is a specialist in analyzing and exploiting data within a company. His/her role is to give meaning to this data, in order to extract value from it, to enable the company to make strategic and/or operational decisions. It is one of the high-responsibility positions in a company.

How much does a Data Scientist charge

The average daily rate of a freelance data scientist is around £450/day. It can vary depending on years of experience and the professional's geographical location. The salary of a junior data analyst is £35K. The salary of an experienced data scientist ranges from £50K to £60K.

What is the definition of a Data Scientist

The Data Scientist is an essential element of the company, given that they are responsible for analyzing massive data called Big Data. They manage the collection, storage, analysis and use of millions of data points collected through different channels. This data is used to analyze company performance, and to anticipate consumer behaviors or new trends. It's both an exciting and promising field. It generates new challenges and allows professionals to continuously gain skills. The Data Scientist has solid knowledge in marketing. This professional is highly sought after by companies that constantly seek to improve their performance and remain competitive. After completing their analysis, the Data Scientist will then write a report explaining their conclusions to management or their client.

What type of mission can a Data Scientist handle

The Data Scientist's main mission is to "predict the future". The significant amount of data that companies now generate can be very useful if used properly. The data scientist can then ensure the strategic development of the company, as well as its digital transformation. To achieve this, they must decipher opaque masses of data to give them meaning. Their role is therefore to transform data into actionable information. They can offer their clients IT management solutions to meet specific needs. Creating algorithms will allow them to anticipate future behaviors and needs in order to guide important decisions. It is this form of creativity that distinguishes them from the Data Analyst. Thanks to their expertise, the Data Scientist must be able to present innovative and relevant proposals to their clients, implement and deploy machine learning models, and finally communicate their conclusions to the relevant departments. They can work on short or medium-term projects.

What are the main skills of a Data Scientist

The Data Scientist must master many skills to effectively carry out their duties. We can mention a few: • Being able to efficiently analyze statistical data and model it • Having good knowledge of programming tools and computer language • Mastering data visualization techniques • Having strong affinities for marketing • Having business sense and good communication skills • Being rigorous, organized, with the ability to make proposals • Being able to maintain data confidentiality • Knowing how to work in a team, under pressure and manage stress • Conducting IT monitoring

What is the ideal profile for a Data Scientist

Many schools are beginning to offer degrees in mathematics and applications with specialization in statistics, Big Data engineering, or massive data analysis. There are also other training programs that provide access to the Data Scientist profession, particularly in higher education schools for computer science, statistics, or engineering schools. However, it will still be necessary to demonstrate 4 to 5 years of experience in data analysis or in a datacenter environment.

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