Data Scientist

Role Overview

We are seeking a highly motivated and analytical Data Scientist to join our team. The ideal candidate will be responsible for analyzing large-scale user and content data, build and scale intelligent systems across recommendation engines, search ranking, churn prediction, and user behavior modeling.

Key Responsibilities

1. Recommendation Systems

• Design and optimize personalized recommendation engines (collaborative filtering, content-based, hybrid models)
• Build real-time and batch recommendation pipelines for playlists, songs, and content discovery
• Improve CTR, session time, and content consumption through personalization

2. Search & Ranking

• Develop and enhance search relevance and ranking algorithms
• Work on query understanding, typo correction, intent detection, and semantic search etc
• Optimize search-to-play conversion and reduce zero-result scenarios

3. Predictive Modelling

Build models for:

• Churn prediction
• User lifetime value (LTV)
• Conversion propensity
• Cohort Management

Drive proactive interventions using predictions

4. Experimentation & Optimization

• Design and run A/B tests and multivariate experiments
• Measure impact using key metrics
• Translate data insights into product and growth strategies

5. Data & ML Infrastructure

• Work with large-scale datasets to build scalable ML pipelines
• Collaborate with engineering teams to productionize models
• Monitor model performance and continuously iterate

Required Qualifications

• 7+ years with Master's degree in Computer Science, Mathematics or a related field
• Hands-on experience with:

Recommendation systems

Search ranking (Elasticsearch, vector search, embeddings)

Predictive modeling techniques

• Solid understanding of:

Machine Learning algorithms

Statistics & probability

Feature engineering & model evaluation

• Strong proficiency in Python & SQL
• Experience with data visualization tools
• Experience with SQL and large-scale data systems (BigQuery, Spark, etc.)
• Familiarity with real-time data processing
• Exposure to MLOps and deployment pipelines

Key Competencies

• Strong problem-solving and analytical thinking
• Ability to communicate complex findings to non-technical stakeholders
• High attention to detail and data accuracy
• Collaborative mindset and ability to work in cross-functional teams