Data Science Intern

1. Applied AI & Model Development

• Develop and deploy machine learning / AI models to enhance platform capabilities.

• Work on use cases such as:

  • Creator segmentation and recommendation.

  • Creator Content labeling and data analysis.

  • Campaign performance prediction and optimization.

  • Agentic Systems Developing.

2. Problem Framing & Experimentation

• Translate business and product problems into data science solutions.

• Design experiments to validate hypotheses and measure impact.

• Iterate on models based on performance, feedback, and real-world usage.

3. Data Pipeline & Feature Engineering

• Work with data engineers to define and build scalable data pipelines.

• Clean, process, and structure large datasets (creator data, content metadata, campaign performance).

• Design meaningful features that improve model performance.

4. Product Integration

• Collaborate closely with product managers and engineers to integrate models into the platform.

• Ensure models are production-ready, reliable, and scalable.

• Contribute to API design, model serving, and inference workflows.

5. Monitoring & Continuous Improvement

• Track model performance using appropriate metrics (accuracy, precision, recall, business KPIs).

• Maintain documentation and ensure reproducibility.

Core Values

• Integrity – You prioritize correctness, transparency, and responsible use of data.

• Gamer Native – You understand gaming ecosystems and player/content dynamics.

• Pop Culture Fluent – You are familiar with creators, platforms, and digital trends.

• Tech-Savvy – You are curious and hands-on with emerging AI technologies.

Technical Skills

• Strong foundation in machine learning and statistics.

• Proficiency in Python and common ML libraries (e.g., PyTorch,scikit-learn).

• Experience with data processing (e.g., pandas, SQL).

• Familiarity with LLMs, NLP, or recommendation systems is a strong plus.

• Understanding of model deployment (APIs, batch/real-time inference) is preferred.

Data & Product Thinking

• Ability to translate ambiguous business problems into data science solutions.

• Strong analytical thinking and hypothesis-driven approach.

• Understanding of how models create value in a product context (not just offline metrics).

Execution & Collaboration

• Ability to work cross-functionally with product, engineering, and business teams.

• Good communication skills—able to explain complex models in simple terms.

• Strong ownership and ability to iterate quickly.

Nice to Have

• Experience in influencer marketing, advertising, or creator platforms.

• Familiarity with social media data (e.g., YouTube, Twitch, TikTok ecosystems).

• Experience with embeddings, vector databases, or retrieval systems.

• Basic knowledge of data engineering or MLOps workflows.

Example Projects You Might Work On

• Build a creator discovery engine using embeddings + similarity search.

• Develop a content understanding pipeline (tags, sentiment, brand safety).

• Train a campaign ROI prediction model to guide budget allocation.

• Integrate LLMs to improve tool efficiency and automate workflows.