- Company Name
- Response Informatics
- Job Title
- Data Science Architect
- Job Description
-
**Job Title**
Data Science Architect
**Role Summary**
Design, implement, and govern end‑to‑end data science solutions across the enterprise. Lead the architecture of model development, deployment, and lifecycle management, ensuring alignment with business objectives and regulatory standards.
**Expectations**
- Deliver scalable, reproducible data science pipelines that support continuous experimentation and model governance.
- Translate complex technical insights into clear, actionable business recommendations for stakeholders.
- Drive adoption of MLOps practices and tooling, improving model delivery velocity and reliability.
**Key Responsibilities**
- Architect and maintain end‑to‑end data science workflows, from data ingestion and feature engineering to model training, testing, and deployment.
- Define and enforce model governance policies, including versioning, audit trails, and compliance checks.
- Evaluate and integrate MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML) into the organization’s technology stack.
- Mentor data science teams on best practices for experimentation, reproducibility, and model monitoring.
- Collaborate with data engineering, product, and business teams to prioritize model initiatives that deliver measurable ROI.
- Monitor deployed models for performance drift and coordinate remediation activities.
**Required Skills**
- 6–10 years in applied data science, machine learning, or analytics leadership.
- Deep knowledge of model lifecycle management, experimentation frameworks, and governance.
- Proficiency in Python, R, SQL, and libraries such as scikit‑learn, TensorFlow, PyTorch.
- Hands‑on experience with MLOps tooling (MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML).
- Strong analytical, communication, and stakeholder‑management abilities.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field.
- Relevant certifications (e.g., TensorFlow Developer, AWS Certified Machine Learning, Azure ML Engineer) preferred.