About the Role
The role involves developing machine learning models to improve content feeds and personalized recommendations by analyzing user interactions and platform behavior.
Responsibilities
- Design and implement machine learning algorithms for content ranking and recommendation
- Analyze large datasets to identify patterns in user engagement and content consumption
- Collaborate with engineering teams to integrate models into production systems
- Evaluate model performance using statistical methods and A/B testing
- Optimize content delivery based on real-time user feedback
- Develop data pipelines for processing high-volume user activity streams
- Improve personalization accuracy by refining feature engineering techniques
- Monitor system performance and troubleshoot model degradation
- Work closely with product teams to align data science solutions with business goals
- Conduct exploratory data analysis to uncover new opportunities in content presentation
- Build scalable solutions for handling diverse content types
- Maintain documentation for models, experiments, and workflows
- Stay current with advancements in recommendation systems and deep learning
- Contribute to the development of internal tools for data analysis and visualization
- Support the creation of metrics dashboards for content performance tracking
- Ensure data quality and integrity across pipelines and models
- Participate in peer reviews of models and analytical approaches
- Help define success criteria for content-related experiments
- Use natural language processing techniques when applicable to content understanding
- Balance personalization with platform-wide content diversity
Compensation
Competitive salary and benefits package
Work Arrangement
Flexible working hours with remote options available
Team
Part of a global data science team focused on user engagement and content delivery
What We Offer
- Opportunities for professional growth in a rapidly evolving industry
- Exposure to large-scale data challenges and cutting-edge technologies
- Collaborative environment with global teams
Application Process
- Candidates will undergo technical assessments and multiple interview rounds
- Final stage includes a presentation of past projects or technical work
Available for qualified candidates
