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
