- Company Name
- Avensys Consulting
- Job Title
- Data Science Specialist
- Job Description
-
Job title: Data Science Specialist
Role Summary:
Evaluate and improve the maturity, structure, and governance of data science practices across multiple teams. Focus on analytical workflows, modeling standards, experimentation culture, and the tangible business impact of data science initiatives.
Expactations:
- 6–10 years of applied data science, machine learning, or analytics leadership experience.
- Proven track record of assessing and designing organizational processes for data science delivery and model management.
- Deep knowledge of model lifecycle management, experimentation frameworks, and data science governance.
- Familiarity with MLOps tooling (MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML) and cloud data science platforms (AWS, Azure, GCP).
- Ability to synthesize technical findings into actionable business recommendations.
- Strong analytical, written, and verbal communication skills.
Key Responsibilities:
1. Assess current data science practices across teams, identifying gaps in structure, governance, and execution.
2. Evaluate analytical workflows, modeling processes, and experimentation setups to ensure quality, reproducibility, and alignment with business goals.
3. Design or refine data science maturity and capability frameworks, and lead their adoption organization‑wide.
4. Advise senior management on strategy, risks, and opportunities related to data science and AI initiatives.
5. Define and enforce model lifecycle management standards, from development to deployment and retirement.
6. Promote a culture of experimentation, evidence‑based decision making, and continuous improvement.
7. Monitor ethical AI compliance, data governance, and regulatory requirements.
8. Collaborate with data, engineering, product, and business stakeholders to align data science outcomes with organizational objectives.
Required Skills:
- Advanced expertise in data science, machine learning, and analytics.
- Proficiency with Python, R, SQL, scikit-learn, TensorFlow, and PyTorch.
- Hands‑on experience with MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML).
- Strong understanding of model lifecycle, experimentation frameworks, and governance.
- Ability to develop and implement maturity models or capability frameworks.
- Excellent communication and stakeholder‑management skills.
- Knowledge of cloud data science environments (AWS, Azure, GCP).
- Familiarity with data governance, compliance, and ethical AI practices.
Required Education & Certifications:
- Bachelor’s or higher degree in Computer Science, Data Science, Statistics, Engineering, or a related field.
- Professional certifications in Machine Learning, MLOps, or cloud platforms (AWS Certified Machine Learning Specialty, Azure AI Engineer Associate, Google Cloud Professional Machine Learning Engineer) are preferred.