ML / Data Science Specialist

Are you a visionary ML/Data Science Specialist who wants to make a tangible impact with data-driven insights and innovative machine learning models? Do you excel at transforming data into actionable strategies that drive business success? Then we want you on our team as an ML/Data Science Specialist! As an ML/Data Science Specialist, you will play a key role in revolutionizing our data strategy and improving our business processes.

This is what awaits you

• Your responsibilities

• Insight-driven optimization: Use data-driven insights to improve the customer experience, optimize dynamic pricing, and enhance logistics or product recommendations.
• Machine learning expertise: Develop and train modern machine learning models for personalization, recommendations, and predictive analytics.
• Prototyping and validation: Create prototypes, refine models with unique data, and validate performance to select the best baseline models or advanced ML algorithms.
• Robust evaluation framework: Create a comprehensive evaluation framework to continuously improve model performance.
• User feedback strategies: Define strategies for collecting user feedback for model tuning and improvement.
• Data exploration: Analyze large data sets to identify trends, patterns, and actionable insights.
• Cloud-based modeling: Build models with ML tools in the cloud, preferably on Google Cloud Platform (GCP).

• ML infrastructure and architecture:

• Scalable ML Platform: Work with the DevOps team to build a scalable ML platform with a model garden and the ability to seamlessly experiment and test.
• Data product creation: Leveraging data from different teams and creating new data products that are published across the organization via a data catalog.

• Operation and optimization:

• Production deployment: Deployment of ML models in production, optimization for scalability.
• Anomaly detection: Monitor model inference, create dashboards and alert systems to inform stakeholders about anomalies.
• Continuous improvement: Regularly monitoring, analyzing, and optimizing the performance, security, and reliability of our ML infrastructure.

• Data management and quality:
• Integrity and security: Ensuring data integrity and security through clear governance policies.

• Data-driven decision making:

• Collaborative Improvement: Work with business teams, such as marketing, to improve processes with ML models and data-driven strategies.
• Automated decision-making: Automate decision-making processes within IT systems using ML models.

• Innovation and technology research:

• Stay informed: Stay up to date with the latest advances in data science, AI, and e-commerce trends.
• Continuous learning: Continuing your education through online courses and self-study.

• Stakeholder management & collaboration:

• Trusted partnerships: Work closely with leaders from engineering, online shops, business applications, and BI to align technical and business goals.
• Effective communication: Communicate effectively at all levels and champion AI-first initiatives with clarity and conviction.