Data Scientist Manager
- Leading end-to-end client projects: from framing use cases with business stakeholders to implementing AI solutions that generate measurable impact.
- Ensuring technical excellence: promote software engineering and MLOps best practices to deliver scalable solutions and bring them into production.
- Supporting solution design: challenging business requirements, validating data availability, and selecting the most appropriate methodologies and architectures.
- Being the technical reference for clients: explaining AI approaches in simple terms, providing recommendations on feasibility, risks, and ROI.
- Developing trusted client relationships: interacting with C-level and operational teams to position Artefact as a long-term partner on their Data & AI journey.
- Support team staffing, ensuring the right skills are applied to meet project needs.
- Ensure each individual's continuous well-being within the team, supporting their growth through mentoring, coaching, and career development plans.
- Help define the team's career framework and participate in performance review processes.
- Challenge existing ways of working and drive innovation to continuously improve Artefact's data science team.
- Contribute to testing and implementing advanced AI approaches and emerging techniques on client use cases.
- Drive initiatives to build internal accelerators, such as modules and tools that can be reused across projects.
- Maintain an active technology watch: evaluate emerging tools, frameworks, and cloud services to assess how they can address client needs.
- Share knowledge through workshops, informal training sessions, and documentation, fostering a culture of continuous learning.
- Lead by example: demonstrate technical rigor, pragmatic problem-solving, and the ability to step in directly on projects when the team needs support.
- Provide constructive feedback to help data scientists in your team progress in both technical and consulting dimensions.
- Anticipate risks and unblock teams, ensuring projects stay on track.
- Balance ambition and feasibility: critically assess approaches, driving innovation while ensuring solutions stay realistic to deliver.
- Foster collaboration between data scientists, data engineers, and business teams to ensure solutions are effectively integrated and adopted.
- 5+ years of hands-on experience developing data-driven solutions.
- Strong knowledge in applied machine learning and generative AI.
- Solid understanding of software engineering and MLOps best practices.
- Experience with industrializing AI workflows in cloud environments.
- Lead teams, manage stakeholders, and deliver impactful projects in a business context.
- Show intellectual curiosity and excellent problem-solving skills.
- Tackle challenges with pragmatism and creativity.
- Support growth, mentoring juniors and constructively challenging seniors.
- Earn trust from both peers and business stakeholders.
- HR call
- Standard Interview
- Technical Interview
- Fit Interview
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