- Company Name
- Photon
- Job Title
- Data Science Architect – Capability and Practice Assessment
- Job Description
-
Job Title: Data Science Architect – Capability and Practice Assessment
Role Summary:
Lead end‑to‑end assessment of data science maturity across the organization, designing structured frameworks, benchmarking best practices, and translating insights into scalable, responsible data‑science operations.
Expectations:
- Deliver clear, actionable maturity scores and improvement roadmaps for at least two cross‑functional data‑science teams per quarter.
- Collaborate with AI and Data‑AI architects to integrate practice findings into a unified capability map.
- Communicate findings effectively to technical and executive stakeholders, ensuring alignment on governance, documentation, and business impact.
Key Responsibilities:
- Evaluate current data‑science processes, tools, and team structures to identify strengths, gaps, and improvement opportunities.
- Design and apply a maturity model covering conception, execution, validation, and scaling of analytical workflows.
- Review model lifecycle practices: data prep, feature engineering, development, validation, monitoring, and iteration.
- Analyze tooling ecosystems, ensuring reproducibility, collaboration readiness, and alignment with MLOps standards.
- Benchmark best practices in experimentation, automation, and MLOps (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML).
- Identify governance, documentation, and model‑to‑business translation gaps and recommend actionable remediation plans.
- Produce comprehensive reports with maturity assessments, gaps, recommendations, and progress tracking.
Required Skills:
- 6–10 years of applied data‑science or analytics leadership experience.
- Deep knowledge of model lifecycle management, experimentation frameworks, and data‑science governance.
- MLOps expertise: MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML (or equivalent).
- Proficiency with Python, R, SQL, and libraries such as scikit‑learn, TensorFlow, PyTorch.
- Strong analytical, problem‑solving, and communication skills; ability to translate technical findings into business‑impact recommendations.
- Experience designing or applying maturity models or capability frameworks for data‑science organizations.
- Understanding of data governance, compliance, and ethical AI practices.
Required Education & Certifications:
- Bachelor’s degree in Computer Science, Statistics, Data Science, or related field (Master’s preferred).
- Relevant certifications (e.g., TensorFlow Specialist, AWS Certified Machine Learning, Azure AI Engineer Associate) are a plus.