Data Science Architect

Job Title: Data Science Architect – Capability and Practice Assessment

The 
Data Science Architect

measures 
how data science is actually done

and 
how mature those processes are

Location:

Hybrid (Paris)

Role Summary

The 
Data Science Architect

will assess the maturity and effectiveness of data science practices across teams, focusing on 
how data science is structured, executed, and governed

within the organization. Unlike the AI Architect, who evaluates individual AI capability, and the Data & AI Architect, who focuses on technical systems and platform maturity, this role centers on 
evaluating analytical workflows, modeling standards, experimentation culture, and applied business impact

.

This position is ideal for someone with a strong background in applied data science, model lifecycle design, and organizational data maturity — capable of analyzing current practices and defining what "best-in-class" looks like for scalable, responsible, and high-impact data science operations.

Key Responsibilities

•
Practice Maturity Assessment:

Evaluate current data science processes, tools, and team structures to determine capability strengths, weaknesses, and improvement areas.

•
Framework Design:

Develop and apply a structured maturity model to assess how data science work is conceived, executed, validated, and scaled.

•
Model Lifecycle Review:

Assess practices across data preparation, feature engineering, model development, validation, monitoring, and iteration.

•
Tooling & Workflow Analysis:

Review the ecosystem of analytical tools, frameworks, and environments used for data science, including reproducibility and collaboration readiness.

•
Benchmarking:

Define benchmarks for best practices in experimentation, automation, and applied machine learning operations (MLOps).

•
Collaboration & Alignment:

Work with AI and Data & AI Architects to connect findings from people, platform, and practice assessments into a unified capability map.

•
Gap Identification:

Identify gaps in model governance, documentation, and model-to-business translation and recommend actionable improvement pathways.

•
Reporting & Advisory:

Produce detailed reports summarizing data science maturity, practice gaps, and recommendations for scaling responsibly and effectively.

Qualifications

• 6–10 years of experience in applied data science, machine learning, or analytics leadership.

• Strong understanding of model lifecycle management, experimentation frameworks, and data science governance.

• Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML).

• Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch).

• Proven ability to assess or design organizational processes for data science delivery and model management.

• Excellent analytical and communication skills, with the ability to synthesize technical observations into actionable business recommendations.

Preferred Skills

• Experience designing or applying data science maturity models or capability frameworks.

• Understanding of data governance, compliance, and ethical AI practices.

• Experience leading or advising multi-team data science organizations.

• Knowledge of cloud data science environments (AWS, Azure, GCP).

Job Title: AI Architect – Talent Assessment and Capability Evaluation

The 
AI Architect

measures 
who

can do AI and 
how they think

about it.

Location:

Hybrid (Paris) Preferred

Role Summary

The 
AI Architect

will play a key role in evaluating organizational AI and data talent readiness. This role focuses on 
assessing technical capability

, 
designing evaluation frameworks

, and 
identifying readiness gaps

across multiple AI and data teams. The architect will define what "good" looks like for AI practitioners at different maturity levels and ensure alignment with the enterprise's overall AI strategy.

This position is ideal for someone with strong architectural judgment, a deep understanding of modern AI ecosystems, and the ability to translate complex technical knowledge into clear evaluation criteria and insights.

Key Responsibilities

•
Capability Framework Design:

Develop and refine the assessment framework for AI and data technical roles — including architecture, MLOps, and applied AI functions.

•
Question and Exercise Creation:

Design technical and scenario-based questions to assess AI understanding, solution design ability, and architectural thinking.

•
Talent Assessment:

Evaluate existing AI talent through structured interviews, workshops, and scorecards to determine readiness and fit.

•
Benchmarking:

Define what "best-in-class" technical architecture looks like within the context of large enterprise AI adoption, referencing current industry practices.

•
Cross-Functional Collaboration:

Partner with program leads and consulting teams to ensure assessments align with capability goals and skill priorities.

•
Gap Identification:

Synthesize findings into insights that highlight capability strengths, weaknesses, and opportunities for targeted training or role refinement.

•
Documentation:

Create clear technical evaluation guides, rubrics, and reports summarizing assessment results and architectural capability maturity.

•
Advisory Role:

Provide thought leadership on AI architecture principles, tooling ecosystems, and governance relevant to enterprise AI scaling.

Qualifications

• 8+ years of experience in AI, data engineering, or software architecture with exposure to enterprise AI ecosystems.

• Strong grasp of AI system design principles (data pipelines, model lifecycle, orchestration, governance) even if not hands-on in implementation.

• Experience conducting technical interviews, designing assessments, or leading capability evaluations preferred.

• Excellent communication and stakeholder engagement skills, with the ability to translate technical complexity into actionable insights.

• Knowledge of cloud-based AI platforms (Azure, AWS, GCP) and enterprise MLOps environments a plus.

• Prior consulting or advisory experience within large organizations is advantageous.