You will be joining a central team of talented Data Scientists, working on a wide variety of impactful & important problem areas like: Search, Personalisation, Content Moderation, Trust & Safety, AI led Automation, Computer Vision, Conversation AI (NLP) etc.
The successful candidate must be able to work directly with very large datasets and data science tools & libraries. He/she should be passionate about their work, detail-oriented, scientific, and have an excellent problem-solving attitude.
You are expected to embody Carousell’s 5 Core Values: Stay Humble, Solve Problems, Be Mission First, Be Relentlessly Resourceful & Care Deeply
Responsibilities:
You will be attached to one of the domains in the Data Science Team (e.g. search / recommendations / computer vision), and will own at least 1 major area of responsibility throughout your 6 months of internships.
There will also be the ability to rotate between different areas of learning should the opportunity arise.
Mine / process data using modern tools and programming languages.
Handle all aspects of ML model development, in partnership and guidance from a Senior Data Scientist
Data wrangling, feature engineering, model exploration / selection, training, offline evaluation, planning A/B experimentation & productionization
Work closely with other data scientists / ML engineers, contributing to the culture of continuous learning & sharing.
Leverage AI-powered tools to improve workflows and insights.
Qualifications
Strong foundational understanding of ML fundamentals and core concepts / architectures
Hands-on experience of solving multiple problems, academic / industry, leveraging machine learning and deep learning
Relevance course of study in a quantitative discipline (e.g. Computer Science, Mathematics, Statistics, or related field).
Good programming ability in Python, SQL and experience with common machine learning frameworks and libraries (e.g. TensorFlow, Keras, Sklearn)
Diligent and reliable, with excellent analytical skills, communication skills, and teamwork
Good to have:
Experience in building ML models at scale, using real-time big data pipelines on platforms such as Spark/MapReduce
Hands on experience of leveraging Deep Learning to solve a business problem
Experience of solving Data Science problems related to eCommerce or classifieds space, esp. Involving user-generated-content.