蚂蚁金服蚂蚁集团-算法专家-杭州
社招全职3年以上技术类-算法地点:杭州状态:招聘
任职要求
1. 计算机、数学、统计、自动化、金融工程等相关专业硕士及以上学历,具备扎实的数据分析与建模能力,有金融、互联网或相关领域实际项目或工作经验; 2. 熟悉机器学习、深度学习、运筹优化或因果推断中至少一个方向,具备以下经验之一者优先: ○ 时序预测、序列建模(如LSTM、Transformer、Prophet等) ○ 运筹优化建模与求解(如线性规划、整数规划、动态规划) ○ 推荐系统、搜索排序、营销自动化 ○ 图神经网络、大模型应用(如微调、Prompt Engineering、Agent设计) 3. 扎实的编程能力,熟练掌握 Python 和 SQL,熟悉常用机器学习框架和优化求解器(如 Gurobi/Cplex); 4. 逻辑清晰,具备良好的问题抽象与系统设计能力,对人工智能技术有热情,具备较强的自驱力、沟通能力和团队协作精神。 5. 加分项:有大模型微调、Agent系统开发、金融时序预测或运筹优化项目经验者优先。
工作职责
1. 流动性算法:综合运用机器学习与运筹优化技术,解决信贷资产预测、资金-资产匹配等关键问题,保障流动性安全与效益最大化; 2. 营销与增长建模:将互联网金融产品的营销、流量分发与用户增长问题抽象为算法问题,通过推荐系统、因果推断、数据挖掘等方法设计并落地算法解决方案; 3. 前沿技术探索:结合信贷与金融市场场景,探索大模型(如AIGC、Agent、LLM for Recommendation)等新技术的可行性与落地路径; 4. 跨团队协作:与产品、数据、风控等团队紧密合作,推动算法模型在真实业务场景中的规模化应用。
包括英文材料
学历+
数据分析+
[英文] Data Analyst Roadmap
https://roadmap.sh/data-analyst
Step by step guide to becoming an Data Analyst in 2025
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
运筹优化+
https://medium.com/gousto-engineering-techbrunch/an-introduction-to-operations-research-5a9e898b6c60
Operations research (OR) is a scientific approach to determining the optimal solution to a defined business problem.
因果推断+
https://web.stanford.edu/~swager/causal_inf_book.pdf
How best to understand and characterize causality is an age-old question in philosophy.
LSTM+
https://colah.github.io/posts/2015-08-Understanding-LSTMs/
Humans don’t start their thinking from scratch every second.
https://d2l.ai/chapter_recurrent-modern/lstm.html
The term “long short-term memory” comes from the following intuition.
https://developer.nvidia.com/discover/lstm
A Long short-term memory (LSTM) is a type of Recurrent Neural Network specially designed to prevent the neural network output for a given input from either decaying or exploding as it cycles through the feedback loops.
https://www.youtube.com/watch?v=YCzL96nL7j0
Basic recurrent neural networks are great, because they can handle different amounts of sequential data, but even relatively small sequences of data can make them difficult to train.
Transformer+
https://huggingface.co/learn/llm-course/en/chapter1/4
Breaking down how Large Language Models work, visualizing how data flows through.
https://poloclub.github.io/transformer-explainer/
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
https://www.youtube.com/watch?v=wjZofJX0v4M
Breaking down how Large Language Models work, visualizing how data flows through.
推荐系统+
[英文] Recommender Systems
https://www.d2l.ai/chapter_recommender-systems/index.html
Recommender systems are widely employed in industry and are ubiquitous in our daily lives.
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
Prompt+
https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/introduction-prompt-design
A prompt is a natural language request submitted to a language model to receive a response back.
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/prompt-engineering
These techniques aren't recommended for reasoning models like gpt-5 and o-series models.
https://www.youtube.com/watch?v=LWiMwhDZ9as
Learn and master the fundamentals of Prompt Engineering and LLMs with this 5-HOUR Prompt Engineering Crash Course!
AI agent+
https://www.ibm.com/think/ai-agents
Your one-stop resource for gaining in-depth knowledge and hands-on applications of AI agents.
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文,免费,零起点,完整示例,基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
SQL+
https://liaoxuefeng.com/books/sql/introduction/index.html
什么是SQL?简单地说,SQL就是访问和处理关系数据库的计算机标准语言。
https://sqlbolt.com/
Learn SQL with simple, interactive exercises.
https://www.youtube.com/watch?v=p3qvj9hO_Bo
In this video we will cover everything you need to know about SQL in only 60 minutes.
Gurobi+
https://gurobi-machinelearning.readthedocs.io/en/stable/user/start.html
A Python package to help use trained regression models in mathematical optimization models.
https://www.youtube.com/watch?v=Er5RM06D9i8&list=PLHiHZENG6W8B_65-Unq-G59PtQtEc1Xj4
This session will provide a brief overview of the training.
CPLEX+
https://home.engineering.iastate.edu/~jdm/ee458/CPLEX-GettingStarted2017.pdf
Describes the components of CPLEX: Interactive Optimizer, Concert Technology, Callable Library.
https://www.ibm.com/docs/en/icos/22.1.2?topic=ide-getting-started-tutorial
A tutorial in which you launch the IDE, create an empty project, enter an OPL model, add data, add a settings file, create run configurations and execute them.
https://www.youtube.com/watch?v=70HH-GNR9uM
Introduction to CPLEX OPL, Work Environment, Creating a new project, run configuration, data and model files.
系统设计+
https://roadmap.sh/system-design
Everything you need to know about designing large scale systems.
https://www.youtube.com/watch?v=F2FmTdLtb_4
This complete system design tutorial covers scalability, reliability, data handling, and high-level architecture with clear explanations, real-world examples, and practical strategies.
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