Job Description

Locations

• Shanghai, Shanghai, China 200335

Salary

Skills Required

• advanced machine learningintermediate
• deep learningintermediate
• statistical modelingintermediate
• analyzing large and complex datasetsintermediate
• extracting valuable insightsintermediate
• building robust modelsintermediate
• collaborating with cross-functional teamsintermediate
• transforming data-driven insights into actionable AI applicationsintermediate

Required Qualifications

• - 3+ years of experience in data science, machine learning, data analysis, or data modeling, with a strong focus on model evaluation, accuracy, and performance metrics. (experience, 3 years)
• - Familiarity with vector similarity search, retrieval-augmented generation(RAG) architectures, and LLM prompt evaluation techniques, with experience in integrating these methods into real-world applications (experience)
• - Advanced programming skills in data manipulation, data processing, and building scalable data pipelines ( SQL & Python preferred). Experience with distributed computing is a plus (experience)
• - Experience crafting, conducting, analyzing, and interpreting experiments and investigations. (experience)
• - Comfort with ambiguity, with the ability to structure complex analysis and drive insights through data exploration and strategy research. (experience)

Preferred Qualifications

• - Experience working with multi-modal foundation models (e.g. GPT-4, Gemini 2.5, Claude 3/4, LLaVA, Flamingo) in practical application such as model training, evaluation, and optimization. (experience)
• - Hands-on experience with LLMs and GenAI frameworks (e.g. LangChain, LlamaIndex) for developing and optimizing AI-driven applications (experience)
• - Familiarity with embedding, retrieval algorithms, agents, and data modeling for vector development graphs. (experience)
• - Proven experience managing complex projects and collaborating across cross-functional teams (experience)
• - Detail-oriented to keep track of and understand the workings of sophisticated algorithms. (experience)
• - Strong experience articulating and translating business questions into data solutions. (experience)
• - Curious, self-motivated, and able to drive improvements to model evaluation pipelines and annotation programs. (experience)
• - Eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment. (experience)
• - Outstanding communication skills – both written and verbal – with experience presenting to leadership. (experience)

Responsibilities

• APO BPR team is seeking a dedicated individual who is passionate about designing, implementing, and optimizing analytical solutions that deliver tangible, measurable impact within APO.
• As an AI Data Scientist, you will leverage advanced machine learning deep learning, and statistical modeling techniques to analyze large and complex datasets, extract valuable insights and build robust models that drive critical business decisions. You will collaborate closely with cross-functional teams to transform these data-driven insights into actionable AI applications, delivering measurable value across the organization.
• In this role, you will
• - Evaluate ML & MM-LLM Models: Analyze, validate, and benchmark computer vision, multi-modal, and large language models(LLMs) to ensure they meet accuracy, robustness, and usability standards, utilizing techniques such as A/B testing and cross-validation, and other model evaluation methods
• - Develop Metrics: Design and implement performance and evaluation metrics to measure model efficiency, accuracy, and scalability in real-world production environments.
• - Failure Analysis: Conduct root cause analysis on model failures across computer vision and multi-modal language model pipelines, identifying improvement areas and collaborating with relevant teams to implement solution.
• - Data Processing: Clean, transform, and curate large-scale structured and unstructured datasets, facilitating efficient model evaluation, benchmarking, and testing across diverse data modalities
• - Model Optimization: Implement innovative model optimization techniques (e.g. model distillation, quantization, pruning) to improve model scalability, performance, and real-world deployment.
• - Collaborate multi-functionally: Collaborate with cross-functional teams, including development, business analysts, and APO teams to integrate models into production.
• - Communicate Results: Communicate findings clearly through technical reports, dashboards, and presentations, tailored to both technical and non-technical audiences.

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