Wavestone
Management Consulting
SeniorConsultant.eDataSciencist(H/F)
“Senior Consultant. e - Data Sciencist (H/F) at Wavestone. Skills: Data Engineering, AI, GenAI, MLOps. Define modern data architectures. Implement modern data architectures”
Industry & Context.
Solution-oriented
What They're Looking For.
Must Have
4 years experience Data Engineering, Pilot Data Engineers teams, Solid mastery modern data architectures, Significant experience one major cloud, Mastery infra-as-code tools, Mastery containerization tools, Good knowledge MLOps practices, Translate business need technical spec, Explain technical trade-offs non-technical, Excellent interpersonal skills, Animate technical workshops, Build consensus, Fluent French, Fluent English
Nice to Have
First concrete experiences LLMOps, PhD preferred, Specific ML framework experience, Cloud platform certs
What You'll Do.
Define modern data architectures
Implement modern data architectures
Structure robust data pipelines
Industrialize AI & GenAI & BI use cases
Transition PoC to production
Implement MLOps best practices
Implement LLMOps best practices
Operate GenAI solution technical components
Define architecture patterns
Define tooling standards
Define development standards
Act as Tech Lead Data Engineering
Frame technical approach
Make architecture trade-offs
Implement data quality best practices
Implement data observability best practices
Contribute to Data & AI governance
Deploy agentic solutions
Design data architecture for agents
Design technical architecture for agents
Integrate agents with business systems
Transform complex processes into agentic workflows
Engage in pre-sales phases
Frame technical approach for pre-sales
Estimate workload for pre-sales
Build Data & GenAI demos
Build Data & GenAI assets
Participate in visibility actions
Share experience feedback
Contribute to BU AI life
Recruit data profiles
Lead internal training
How You'll Work.
Team & Collaboration
Client teams; Data Scientists; AI Engineers; Data & AI Platform teams; Data Platform Engineers; DevOps; Architects; Security; AI squads; AI Factory squads; Business teams; IT teams; Data Office teams; Security teams; Business stakeholders
Communication Scope
Explain technical trade-offs
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