Data Scientist (LLM&Unsupervised Learning) - Applied AI

oduct, operations, policy, and engineering teams to leverage advanced natural language processing, computer vision, and deep learning technologies to solve business problems and extract data insights.

Responsibilities:

1. Leverage multimodal large language models, natural language processing, machine learning, or computer vision techniques to design and build core product capabilities, extract insights, and optimize monetization strategies;
2. Develop innovative algorithms and build prototypes for business problems using the latest deep learning, machine learning, statistical, and optimization techniques;
3. Use unsupervised learning and clustering algorithms to discover potential patterns and trends from large datasets and propose data-driven business solutions;
4. Collaborate with product managers and cross-functional teams to define user stories and success metrics, managing data projects from 0 to 1;
5. Use methods like AB testing to validate the business value and expected revenue of projects and continuously optimize model performance;
6. Work with engineering teams to deploy data models and scale solutions.

Qualifications

Minimum Qualifications:

1. In-depth knowledge of computer science and the mathematical fundamentals of statistics, machine learning, and analytics;
2. At least 3 years of experience in software development or model/data development, with hands-on experience in applying LLM technologies (such as Test Time Scaling, Chain of Thought, Retrieval Augmented Generation, Supervised Fine-Tuning) to solve business problems;
3. Strong experience with unsupervised learning, clustering algorithms, and extracting data insights, recognizing patterns, and developing models;
4. Proficiency in Python and SQL, with experience in ML/DL frameworks like TensorFlow, PyTorch;

Preferred Qualifications:

1. Expertise in SQL, Hive, Presto, or Spark, and experience with large-scale datasets;
2. Solid understanding of building data pipelines, model development, testing, and deployment;
3. Experience with CI/CD (such as git) and cloud services (such as AWS/GCP/Azure) is a plus;
4. Strong English communication skills, with the ability to clearly explain technical and analytical content to both technical and non-technical teams;
5. A strong intellectual curiosity, excellent problem-solving and quantitative analysis skills, with the ability to deconstruct issues, identify root causes, and propose solutions.