Responsibilities

• Build statistical and machine learning models to analyse large (PB-scale) volume of customer data, uncovering valuable insights and patterns
• Design and implement experiments, including A/B tests and causal inference to measure the impact of different strategies, initiatives, and product changes on user behaviour and business outcomes
• Implement robust MLOps pipelines to automate the deployment, monitoring, and maintenance of models in production environments
• Effectively communicate complex findings and insights to both technical and non-technical stakeholders
• Stay updated with the latest advancements in AI and Generative AI technologies
• Build Big Data pipelines to support data science-related projects

Requirements

• Bachelor's degree or above in Statistics, Applied Mathematics, Computer Science or a related field
• 3-5+ years of working experience in Data Science, machine learning and deep learning
• In-depth understanding of machine learning and deep learning algorithms
• Hands-on experience in developing and deploying machine learning/deep learning models in production
• Experience with common analysis tools(SQL/ Python)
• Familiar and comfortable with large volumes of data and big data technology (Hadoop/Spark/Hive)
• Experience in using deep learning frameworks(Tensorflow/PyTorch)
• Solid technical & knowledge of A/B testing methodologies, can consistently explore and find the best practice
• Self-direction and willingness to both teach others and learn new techniques
• Knowledge and affinity of GenAI libraries and toolsets including HuggingFace, LangChain, RAGAS, and more is a plus
• Hands-on experience in RAG and fine-tune LLMs is a plus
• Have a strong ability to work under pressure, have the courage to overcome difficulties, and accept challenges