Data Science Architect Capability and Practice Assessment

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

610 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).