Data Scientist – ML Engineering
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Job Description
Data Scientist focusing on ML Engineering at Wizeline, developing AI solutions for clients. Collaborating with teams and leading stakeholder discussions on AI strategies for effective implementation.
Responsibilities:
- Develop and implement machine learning models and solutions.
- Collaborate with cross-functional teams to drive AI initiatives.
- Optimize and enhance ML systems for scalability.
- Lead discussions with stakeholders regarding AI strategies and solutions.
- Mentor junior team members in ML best practices.
Requirements:
- Bachelor's required; Master's preferred in CS, Engineering, or related.
- 5–8+ years in ML Engineering, MLOps, or high-scale ML systems.
- Deep expertise in Spark, Azure Databricks, MLflow, Kubernetes, and Docker.
- Proven track record deploying ML at enterprise scale with audit and monitoring layers.
- Familiarity with hybrid/multi-cloud infrastructure.
- Strategic, persuasive, and business-oriented communication.
- Strong storytelling to explain, defend, and "sell" complex solutions.
- Ability to lead complex conversations with clients and senior stakeholders.
- Clear, structured, and confident responses to objections or ambiguous scenarios.
- Ability to translate technical topics into business impact and decision-making.
- Ability to build trust, credibility, and alignment through communication.
- AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows.
- Leadership experience in ML platform or DevOps teams.
- Experience with feature stores and feature engineering. AutoML is a plus, H2O is a plus.
Benefits:
- A High-Impact Environment
- Commitment to Professional Development
- Flexible and Collaborative Culture
- Global Opportunities
- Vibrant Community
- Total Rewards
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