About the Team
You will be a member of the core end-to-end AI team, from BI to AI. You will be responsible for democratising insights on our core businesses, collaborating with cross-functional teams to define, measure, and understand key metrics, influence business strategy and product roadmap through data, deliver analysis reports and models, empower our products and services with data-driven and AI-driven approaches, and significantly impact the business. You will collaborate closely with the engineering team, product team, and other stakeholders in defining the data collection needs, the data schema, the data analysis, the model training, and the model deployment.
Responsibilities
- Work on large-scale structured and unstructured data sets to solve a wide array of challenging problems using analytical, statistical, machine learning, or deep learning approaches.
- Work with stakeholders from different departments to understand their business needs and challenges, architect and design analytics solutions to meet business objectives and support business decision-making.
- Collaborate with cross-functional stakeholders to provide strategies based on data-driven insights across product, marketing, compliance, and others.
- Define, understand, and test external/internal opportunities to improve our products and services.
- Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics.
- Develop data models, data automation systems, performance metrics, and reporting systems, and track impact over time.
- Deliver Machine Learning projects from beginning to end. This includes understanding the business need, planning the project, aggregating & exploring data, building & validating models, and deploying ML models to deliver business impact.
- Improve upon existing machine learning methodologies by developing new sources, developing and testing enhancements, running computational experiments, and fine-tuning parameters.
- Communicate results and business impacts of insight initiatives to stakeholders within and outside of the company.
Requirements
- Bachelor degree in Business, Economics, Statistics, Data Science, Data Mining, or similar quantitative field.
- Proven successful and trackable experience in an analytical role or data scientist role involving extraction, analysis, and/or modeling.
- Solid experiences in SQL, familiar with SQL functions such as window functions and aggregate functions.
- Solid experiences in Python, familiar with data analysis libraries such as pandas, numpy, matplotlib, scikit-learn, etc.
- Experience in using analytical concepts and statistical techniques: hypothesis development, designing tests/experiments, analysing data, drawing conclusions, and developing actionable recommendations for business units.
- Experience in developing production-grade ML systems including exploratory analysis, feature engineering, hyperparameter tuning, model training, model selection, creating data pipelines, etc.
- Experience in working with deep learning frameworks such as PyTorch for real-world problems.
- Knowledge of Computer Science fundamentals such as object-oriented design, graph algorithm, algorithm design, data structures, problem solving and complexity analysis.
- Self-driven, innovative, collaborative, with good communication and presentation skills, able to translate between business and technical audiences.
Preferred Qualifications
- Master or Phd in Machine Learning, Applied Mathematics, Statistics, Data Mining, Computer Vision, Computer Science, Business, Economics, or other quantitative fields.
- Experience in FinTech, eCommerce, SaaS, AdTech, or Digital Wallet business industries.
- Experience in working with teams across offices and timezones is a plus.
- Experience in big data tools such as Amplitude, DataWorks, MaxCompute, Hadoop, Hive, Spark and HBase is a big plus.
- Experience using analytics techniques to contribute to company growth or increasing revenue and other key business outcomes is a big plus.
- Experience with recommendation systems is a plus.
- Experience with RPA system is a plus.
- Experience with NLP, such as NER, semantic analysis and machine translation, etc. is a plus.
- Experience with CV, such as image classification, image detection, OCR, etc. is a plus.
- Experience in Triton Inference Server or ONNX is a plus.
- Experience with application development practices at scale.
- Publication at top AI conferences and journals.