Love buying and selling on Carousell? Then meet the team that handcrafts various parts of the mobile applications, website and backend systems in order to deliver the best user experience. Here at Carousell, our data & engineering teams work on a myriad of problem domains. You get to work with data generated by millions of buyers and sellers & converting them into actionable insights, for both our users and team. You will have the autonomy to lead and explore data science projects to solve key business problems, by working together with a core team of passionate data analysts, scientists and engineers. Every month, we host a range of meetups and talks on different topics, ranging from product hackdays to a Swift workshop by the engineering team members!
Ensuring that the user experience stays simple is complicated - and we take pride in our work to keep things that way.
Key features within Carousell includes: search, discovery, chat, inbox, selling experience, profile management, payments, review system and many more.
- Perform fundamental and applied machine learning in large-scale distributed analysis, streaming data analysis, time-series and feature discovery
- Create predictive models for user behavior using any and all data available in our clusters
- Build, validate, test, and deploy machine learning models (e.g. predictive, forecasting, clustering) using proven and experimental techniques
- Work with engineering to implement predictive models in production environment
- Initiate high impact data science projects and with actionable outcomes
- Provide expertise on concepts for machine learning and applied analytics for the data team and inspire the adoption of data science across the breadth of our organization
- Proficient in data science languages such as Python, R, or Scala
- Familiar with general ML methods and expert in one of the following field: Recommender, Supervised learning and feature engineering, Personalization
- Proficiency and strong experience with predictive problems using techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks.
- A self-motivated and independent learner driven by your curiousity in what you may uncover
- Happy to learn from and share knowledge with team members
- A doer with a 'get it done' attitude
Good To Have:
- Experience in building ML models at scale, using real-time big data pipelines on platforms such as Spark/MapReduce
- Proficiency in implementation of machine-learning algorithms (DNN, CNN) in support of: Computer vision and object recognition/identification, Natural language processing
- Please send us your Kaggle and/or Github profile if any!