melotech - Data Scientist
Requirements
• We look for battle-tested data scientists who understand that real impact comes from turning messy, unstructured data into business-critical insights that drive company direction. You don't just build models: you solve business problems by finding creative ways to access hard-to-reach data and translating statistical findings into actions the team takes. • Typically, your profile will look like this: • Background: a degree in Data Science, Statistics, Computer Science, Physics, Math or a related quantitative field • Experience: 3 or more years of hands-on data science in fast-paced business environments, at Tier 1 consultancies, Big Tech, high-growth start-ups and scale-ups, or media industry leaders • Python: expert-level proficiency with the standard data science and ML frameworks, and SQL as a daily tool • Data acquisition: you have scraped and acquired raw data from new sources, building creative solutions to reach datasets beyond standard APIs • Statistical rigour: you design and read experiments properly, you know when a signal is noise, and you have run A/B tests or causal analyses that changed a decision • Models in production: you have deployed models that people actually used, and you know what it takes to keep them honest over time • AI-native: extensive hands-on experience with agentic coding tools and LLMs in your data workflows
Responsibilities
• New data sources: discovering and opening up untapped sources, with creative scraping and acquisition techniques when standard APIs run out • Trends and audiences: analyzing trends, audience behavior and performance across streaming and social to find the underserved categories and the next growth opportunity, and proposing the next step • Prediction: prototyping and deploying ML models on AWS for virality prediction and content analysis, and checking whether they hold up in production • Experiments: designing and reading A/B tests, holdouts and spend experiments across our releases and campaigns, and telling the team where the next dollar should go • Numbers people trust: owning the dashboards and reports leadership runs on, and investigating what really drives a metric when it moves • AI-native workflows: using LLMs and agentic coding tools to accelerate data cleaning, analysis and reporting
Benefits
• Internship/ Full-Time
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