Algorithmic Problem Solving & Coding
This area evaluates your fundamental programming skills and your ability to write clean, efficient code to solve structured problems. Rakuten Symphony expects its data scientists to write production-grade code, meaning your algorithms must be optimized for both time and space complexity.
During this round, you will face live coding challenges centered around core data structures. The interviewer is not just looking for a working solution; they are evaluating how you structure your code, handle edge cases, and discuss trade-offs.
Be ready to go over:
- Data Structures – Deep familiarity with arrays, linked lists, hash maps, trees, and stacks.
- Time and Space Complexity – Calculating Big O notation for every solution you propose.
- Python Optimization – Utilizing built-in libraries, list comprehensions, and memory-efficient generators.
Example scenarios:
- Reversing a linked list or detecting cycles within a data stream.
- Finding the first non-repeating character in a massive string of network logs.
- Implementing an efficient binary search algorithm on a sorted list of telemetry timestamps.
Core Machine Learning & Statistical Modeling
This evaluation area is an intensive, deep dive into theoretical and practical machine learning. Interviewers will question you thoroughly on every aspect of supervised learning, ensuring you understand the "why" behind model behaviors rather than just importing libraries.
You will need to demonstrate a comprehensive understanding of statistical concepts, algorithm mechanics, and model evaluation metrics. A strong performance in this area requires you to explain complex mathematical formulations in simple, intuitive terms.
Be ready to go over:
- Supervised Learning Fundamentals – Linear regression, logistic regression, decision trees, support vector machines, and ensemble methods.
- Model Evaluation – Precision, recall, F1-score, ROC-AUC, and choosing the right metric for imbalanced datasets.
- Regularization & Optimization – Gradient descent, learning rates, overfitting prevention, and hyperparameter tuning.
- Advanced concepts (less common) – Graph Neural Networks (GNNs) for network topology, semi-supervised learning for unlabeled telecom logs, and custom loss function design.
Example scenarios:
- Explaining how you would design a classification model to detect network cell failures when only 0.1% of the data contains failure events.
- Walk through the mathematical steps of how a decision tree decides where to split a continuous feature.
- Discussing how you would handle multicollinearity among highly correlated telecom performance metrics.
Data Engineering & Scalability
At Rakuten Symphony, data science and data engineering are deeply intertwined. You must prove that you can design robust data pipelines to ingest, transform, and store massive datasets before feeding them into your machine learning models.
This area evaluates your familiarity with distributed systems, cloud infrastructure, and databases. You should be comfortable discussing how to scale algorithms to run on billions of rows of data.
Be ready to go over:
- Distributed Computing – Core concepts of Apache Spark, Hadoop, MapReduce, and query optimization in SQL or Hive.
- Data Pipelines – Designing ETL/ELT processes using orchestration tools like Apache Airflow.
- Database Architecture – Working with relational databases, NoSQL systems, and modern Vector Databases for AI applications.
Example scenarios:
- Designing an ETL pipeline that processes real-time streaming data from Kafka and stores it in a data lake for daily model training.
- Optimizing a slow-running SQL query that joins a massive network traffic table with a user metadata table.
- Explaining how you would partition a database to improve query performance for geographic coordinates.
