Data Science Specialist
Posted 1 week ago by Response Informatics
Negotiable
Undetermined
Undetermined
London Area, United Kingdom
Summary: The Data Science Architect will evaluate the maturity and effectiveness of data science practices within the organization, focusing on analytical workflows, modeling standards, and business impact. This role requires a strong background in applied data science and model lifecycle design to define best practices for scalable and high-impact operations. The position emphasizes collaboration with other architects to create a unified capability map and identify areas for improvement. Ideal candidates will possess the ability to analyze current practices and recommend actionable pathways for enhancement.
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.
Key Skills:
- Strong background in applied data science.
- Experience in model lifecycle design.
- Knowledge of organizational data maturity.
- Ability to analyze current practices and define best-in-class standards.
- Experience in evaluating analytical workflows and modeling standards.
- Familiarity with data science tools and frameworks.
- Strong reporting and advisory skills.
- Collaboration skills to work with various architects.
Salary (Rate): undetermined
City: London Area
Country: United Kingdom
Working Arrangements: undetermined
IR35 Status: undetermined
Seniority Level: undetermined
Industry: Other
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.
- 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.