Data Scientist - Applied AI

About the Role:
We are looking for an AI/ML technical expert, focusing on the application of multimodal LLMs, unsupervised learning, and clustering algorithms. The candidate will work closely with product, 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.

What You'll Do:
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.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.