Data Scientist

Key Responsibilities

• Data Analysis & Research: Analyze large datasets using queries and scripts to extract meaningful insights and identify opportunities for improving complex ML and bidding systems.
• Simulation & Modelling: Design and execute simulations to validate hypotheses, quantify efficiency gains, and model system performance.
• Experimentation & Causal Inference: Develop robust experiment designs and metric frameworks to deliver unbiased, data-backed insights for product and business decisions.
• End-to-End ML Deployment: Build, train, and deploy ML models into production environments, managing the full lifecycle including versioning, monitoring, and retraining.
• Scalability & Performance Optimization: Operationalize ML models at scale, optimizing for performance, reliability, and cost efficiency in real-world production systems.
• Cross-Functional Collaboration: Work closely with product, engineering, and data teams to translate business problems into analytical solutions.

Key Qualifications

• Master’s degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Data Science) or equivalent experience.
• 3+ years of professional experience in data science or applied machine learning.
• Strong problem-solving and analytical skills, with the ability to turn complex product questions into actionable insights.
• Excellent communication skills, both verbal and written, with the ability to present technical results to non-technical audiences.
• Proven ability to build and maintain strong relationships with stakeholders across teams and functions.
• Deep understanding of machine learning algorithms, from classical methods (e.g., regression, random forests, k-means) to advanced techniques (e.g., gradient boosting frameworks such as XGBoost, LightGBM, CatBoost, and transformer-based architectures like BERT or Sentence Transformers).
• Proficiency in Python or R, and data manipulation tools/libraries such as Pandas and SQL.
• Hands-on experience deploying models in production and managing ML lifecycle processes (monitoring, retraining, version control).

Preferred Skills

• Experience with cloud platforms (GCP, AWS, or Azure).
• Familiarity with MLOps frameworks for deployment, monitoring, and automation.
• Exposure to big data tools (e.g., Spark, BigQuery).
• Understanding of A/B testing, experimentation frameworks, and causal inference techniques.