About OLX Group
Over 300M monthly active users; US $1.6 billion in revenue and 18% revenue growth (FY 2021; 36% growth in FY2020); Part of Naspers’ Prosus, one of the biggest technology investors in the world (An early investor in Tencent and the owner of StackOverFlow). +30 countries. +20 Brands. Powered by +10,000 employees representing 81 nationalities.
Part of the OLX Group, with 5,000+ employees and 500+ inspection centers across the Americas, and Asia, OLX Autos manages a truly global car marketplace. OLX Autos is the smart way to sell your car. We offer one-stop solutions that are safe, and convenient, and offer guaranteed peace of mind for both buyer and seller.
Role And Responsibilities
- Analyze data to define and deliver on complex analytical deep dives to unlock insights and build scalable solutions through Data Science to ensure DF transactions and business in OLX.
- Build Machine Learning and/or statistical models that evaluate the dealer’s applications.
- Define and conduct experiments to validate/reject hypotheses, and communicate insights and recommendations to Product and Credit teams.
- Performs research and applies new techniques and concepts to solve problems.
- Understands and translates business and functional needs into AI/ML problem statements.
- Develop custom ML models to drive innovative business solutions.
- Manages and integrates complex data sets from multiple, disparate data sources.
- Writes complex queries and extracts data to build robust data pipelines.
- Performs exploratory data analysis and develops models to identify trends and opportunities.
- Develop efficient data querying infrastructure for both offline and online use cases.
- Provide mentorship and technical guidance to Data Scientists on the team.
Qualifications
- Experience ranging from 3 to 5 years in related expertise of data science will be the minimum expectation.
- Thorough understanding of the Application / Behaviour / Collection scorecard development process.
- Proficiency in machine learning algorithms such as decision trees, support vector machines, Gradient Boosting Machines (GBM), Random Forest, Regularized regression, models, time series forecasting, anomaly detection, etc.
- Strong understanding of probability and statistical models (generative and descriptive models).
- Ability to run experiments scientifically and analyze results.
- Ability to effectively communicate technical concepts and results to business audiences in a comprehensive manner.
- Ability to collaborate effectively across multiple teams and stakeholders, including analytics teams, development teams, product management, and operations.
- Experience with Big Data processing (Spark/Bigquery / Hive/ Hadoop/ HDFS).
- Experience with productionizing Machine Learning solutions and CI/CD.
- Comfort level with cloud computing (e.g. Azure, Google, etc.).
- Proficiency in coding (Python, R, SQL) is a must.
- Understanding of decisions and portfolio management in banking and financial services would be added advantage.
Education
- Master’s or any other graduation degree with related statistics or computer science or data science background would be preferred.