{"id":218422,"date":"2020-09-28T00:00:00","date_gmt":"2020-09-27T22:00:00","guid":{"rendered":"https:\/\/blog.lewagon.com\/non-classifiee\/working-ai-data-teams-advice-manager-deloitte\/"},"modified":"2020-09-28T00:00:00","modified_gmt":"2020-09-27T22:00:00","slug":"working-ai-data-teams-advice-manager-deloitte","status":"publish","type":"post","link":"https:\/\/blog.lewagon.com\/fr\/career\/working-ai-data-teams-advice-manager-deloitte\/","title":{"rendered":"Working in AI & data teams: valuable advice from an AI manager at Deloitte"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"

Ghislene Zerguini \u00e9tait notre invit\u00e9e lors du Women\u2019s Data day organis\u00e9 au mois de Septembre. Son intervention a \u00e9t\u00e9 des plus inspirantes. 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Zerguini \u00e9tait notre invit\u00e9e lors du Women\u2019s Data day organis\u00e9 au mois de Septembre. Son intervention a \u00e9t\u00e9 des plus inspirantes. Manager chez Omnia AI, la division Intelligence Artificielle de Deloitte, elle est \u00e9galement co-fondatrice de la branche montr\u00e9alaise de WiMLDS (Women in Machine Learning and Data Science).","_yoast_wpseo_title":"Working in AI & data teams: valuable advice from an AI manager at Deloitte | Le Wagon","contenu_de_larticle":"

\n Ghislene a partag\u00e9 avec nous de pr\u00e9cieux conseils concernant le march\u00e9 de l'emploi dans les domaines de l'IA et des donn\u00e9es: nous avons \u00e9chang\u00e9 sur certaines id\u00e9es pr\u00e9con\u00e7ues qui touchent l'industrie, la structure des \u00e9quipes data, les derni\u00e8res tendances et les conseils pour les personnes qui souhaitent changer de carri\u00e8re. Si vous \u00eates \u00e0 la recherche de nouvelles opportunit\u00e9s dans l\u2019univers des donn\u00e9es et que vous h\u00e9sitez d\u00e9marrer une \n formation Data Science<\/a>, cet article est fait pour vous. \n
\n
\n<\/div>\n

Quelles sont les id\u00e9es pr\u00e9con\u00e7ues qui touchent l\u2019industrie ? <\/h2>\n
\n
\n<\/div>\n
\n Les gens pensent souvent qu\u2019il faut n\u00e9cessairement avoir une bonne ma\u00eetrise d\u2019au moins un langage de programmation ou \u00eatre tr\u00e8s technophile pour travailler dans l\u2019univers de l\u2019intelligence artificielle, mais ce n\u2019est pas toujours le cas. Il faut certes des profils techniques au sein des \u00e9quipes d'IA (comme par exemple des \n d\u00e9veloppeurs data IA<\/a>), mais chaque \u00e9quipe a besoin de personnes avec des connaissances sur le domaine d'activit\u00e9s de l'entreprise, capables de traduire la th\u00e9orie en pratique. \n<\/div>\n
\n
\n<\/div>\n
\n Les profils venant du monde des affaires sont excellents pour traduire les informations technologiques en plans d\u2019actions. Si tu es un.e professionnel.le au profil hybride, \u00e0 la fois tech et business, alors tu as toute ta place dans une \u00e9quipe data. \n<\/blockquote>\n
\n
\n<\/div>\n
\n L\u2019autre id\u00e9e pr\u00e9con\u00e7ue dans le domaine: qui dit donn\u00e9es dit IA, alors qu'en fait, la plupart des projets \u00abdata\u00bb ne rel\u00e8ve pas n\u00e9cessairement de l'intelligence artificielle. Il existe beaucoup de projets qui font appel \u00e0 l'analyse avanc\u00e9e mais qui n\u2019ont rien avoir avec l\u2019intelligence artificielle. Il est important de comprendre et s\u00e9parer ces concepts.\n<\/div>\n
\n
\n<\/div>\n

Pourquoi les \u00e9quipes hybrides sont-elles si populaires?<\/h2>\n
\n
\n<\/div>\n
\n Je suis un grand fan d'\u00e9quipes \u00abhybrides\u00bb, le travail est fondamentalement meilleur si les gens ne pensent pas de la m\u00eame fa\u00e7on dans une \u00e9quipe. Si vous gardez une communication ouverte et que vous faites cela de mani\u00e8re constructive, il est pr\u00e9f\u00e9rable de travailler avec des personnes ayant une perspective diff\u00e9rente de la v\u00f4tre.\n<\/div>\n
\n
\n<\/div>\n
\n Pour construire un bon produit en IA, une \u00e9quipe doit avoir des gens d'affaires (ou comme on dit dans le jargon, rouge), des personnes au profil tech (bleu) et des profils hybrides comme les chefs de projet, les experts en la mati\u00e8re ou les conseillers (violet).\n<\/div>\n
\n
\n<\/div>\n

Comment pallier le manque de comp\u00e9tences techniques ou business?<\/h2>\n
\n
\n<\/div>\n
\n J\u2019ai un profil plut\u00f4t technique et la plupart des comp\u00e9tences g\u00e9n\u00e9rales que j'ai acquises, c\u2019est gr\u00e2ce au travail b\u00e9n\u00e9vole. C'est une excellente option pour apprendre de nouvelles choses. Vous pouvez mettre \u00e0 profit vos connaissances tout en apprenant une nouvelle comp\u00e9tence. Par exemple, je fais partie de \u00ab \n Quotient social <\/strong>\u00bb, une organisation \u00e0 but non lucratif qui aide les ONG avec leurs donn\u00e9es. La plupart des b\u00e9n\u00e9voles sont des professionnels r\u00e9cemment dipl\u00f4m\u00e9s. Si vous \u00eates bon techniquement mais manquez d'exp\u00e9rience en affaires, c'est une des choses que je vous conseillerais de faire. \n<\/div>\n
\n
\n<\/div>\n
\n Si vous recherchez \u00e0 acqu\u00e9rir des comp\u00e9tences techniques, rejoindre des programmes d'\u00e9ducation comme Le Wagon est \u00e9galement une autre excellente option. Il existe aussi des groupes tels que WiMLDS (Women in Machine Learning and Data Science) dans lequel vous pouvez continuer \u00e0 apprendre. Je suis l'une des fondatrices de ce groupe \u00e0 Montr\u00e9al. C'est une plateforme pour connecter les femmes dans l'apprentissage automatique et la science des donn\u00e9es. Nous avons ouvert le chapitre \u00e0 Montr\u00e9al parce que nous nous sommes rendu compte que les femmes \u00e9taient tr\u00e8s peu repr\u00e9sent\u00e9es dans les \u00e9v\u00e9nements li\u00e9s aux donn\u00e9es et nous voulions cr\u00e9er un espace d\u2019\u00e9change et de collaboration. \n<\/div>\n
\n
\n<\/div>\n

Quelles sont les comp\u00e9tences les plus demand\u00e9es au sein des \u00e9quipes data ? <\/h2>\n
\n
\n<\/div>\n
\n Dans mon \u00e9quipe (Omnia - Deloitte), nous avons toujours des professionnels des affaires, des experts dans leurs domaines travaillant avec des experts en technologie. Nous avons principalement des professionnels techniques dans l'\u00e9quipe, mais les experts commerciaux nous aident beaucoup \u00e0 souligner les tendances et ils ont besoin de techniciens pour s'assurer que les donn\u00e9es correspondent \u00e0 leurs pr\u00e9visions. Cette collaboration nous aide \u00e9galement \u00e0 prendre des d\u00e9cisions \u00e9clair\u00e9es. \n
\n
\n<\/div>\n

Comment est structur\u00e9e ton \u00e9quipe chez Deloitte?<\/h2>\n
\n
\n<\/div>\n
\n Cela d\u00e9pend du projet. Je travaille actuellement sur un projet gouvernemental. Nous avons 4 d\u00e9veloppeurs travaillant avec Python et SQL et 8 fiscalistes avec une bonne ma\u00eetrise m\u00e9tier et ainsi que des besoins du projet. Nous avons \u00e9galement 2 personnes hybrides qui s\u2019occupent plus de la partie paie et structure des donn\u00e9es.\n<\/div>\n
\n
\n<\/div>\n

Quelle est la chose la plus int\u00e9ressante dans ton m\u00e9tier ?<\/h2>\n
\n
\n<\/div>\n
\n La diversit\u00e9 des projets! Je n\u2019aimerais pas travailler pour une  seule industrie, ayant les m\u00eames d\u00e9fis tout le temps. Avant de travailler dans ce projet gouvernemental, je travaillais pour diff\u00e9rents secteurs tels que le commerce de d\u00e9tail, la pharmacie, la finance, l'assurance. \u00c7a change constamment et je ne m'ennuie jamais.\n<\/div>\n
\n
\n<\/div>\n

Y a-t-il des domaines en IA qui croissent plus vite que d'autres?<\/h2>\n
\n
\n<\/div>\n
\n L'une des tendances que nous observons est la forte croissance de la partie architecture des projets. Avec toute la quantit\u00e9 de donn\u00e9es qui augmente, nous constatons beaucoup de changements et une augmentation du point de vue des strat\u00e9gies architecturales.\n<\/div>\n
\n
\n<\/div>\n

Quels conseils donneriez-vous aux personnes qui cherchent \u00e0 faire une transition vers des postes en IA ou en data?<\/h2>\n
\n
\n<\/div>\n
\n Premi\u00e8rement : n'h\u00e9sitez pas \u00e0 parler aux gens sur Linkedin. L'une des choses que nous avons tendance \u00e0 penser est que nous d\u00e9rangeons les gens avec cette d\u00e9marche. Mais si vous r\u00e9fl\u00e9chissez \u00e0 un changement de carri\u00e8re et \u00e0 une reconversion dans l'un des \n m\u00e9tiers de la Data Science<\/a>, identifiez quelqu'un qui exerce le travail de vos r\u00eaves et envoyez-lui un message, la plupart des gens seront heureux d\u2019\u00e9changer sur leur parcours autour d\u2019un caf\u00e9.\n<\/div>\n
\n
\n<\/div>\n
\n Deuxi\u00e8mement, essayez de vous pr\u00e9parer avant de d\u00e9marrer la recherche d\u2019un emploi. Faites une liste de vos forces et de vos faiblesses et aussi, \u00eates-vous plut\u00f4t technique ou plut\u00f4t business ? Ou peut-\u00eatre aimeriez-vous une position hybride ? Vous devez savoir ce que vous apportez \u00e0 la table. Si vous souhaitez changer de carri\u00e8re, pensez \u00e0 ces diff\u00e9rentes possibilit\u00e9s.\n<\/div>\n
\n
\n
\n
\n<\/div>\n
\n
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