滴滴信贷增长算法专家(J250704016)
社招全职3年以上技术地点：北京状态：招聘
任职要求
1、本科/硕士及以上学历，计算机、数据科学、统计学、应用数学等相关专业优先，有机器学习、推荐系统、优化算法、因果推断、运筹优化等相关知识储备； 2、具备良好的沟通协作能力，数据驱动思维，逻辑清晰，能够主动推动跨团队合作，解决业务问题。 3，熟悉 Python/Java/C++，掌握 TensorFlow/PyTorch、Sklearn、Spark 等常见 AI 框架，有大规模数据建模经验； 4、具备扎实的算法基础，熟悉常见的机器学习/深度学习算法，如 LR、XGBoost、DeepFM、Transformer、LSTM 等，并能根据业务需求进行模型优化； 5、对增长算法、推荐系统、广告投放、精准营销等领域有相关项目实践，具备较强的数据分析能力和业务理解能力； 6、优先：在信贷增长营销、风险建模等有3年以上经验；优先 有CCF-A类论文发表经验。
工作职责
1、用户增长算法：基于信贷业务，结合用户画像、用户行为等构建用户增长策略及体系，通过流量运筹等手段实现高效用户拉新和促活； 2、流量算法：熟悉常用的流量优化模型，渠道优化模型，渠道运筹能力等。 3、营销算法：补贴及激励能力建设，持续优化ROI及营销效率，提升营销精准度； 4、信贷经营模型：理解信贷全流程，深入理解并挖掘信贷及多方数据，通过价格等手段提升用户转化。 5、大模型/AI 赋能营销：探索大模型（NLP、AIGC）在短信内容生成、AI 外呼话术优化、用户情绪识别等场景的应用，提升触达效率与用户体验；
包括英文材料
学历+
数据科学+
https://roadmap.sh/ai-data-scientist
Step by step roadmap guide to becoming an AI and Data Scientist
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.
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.
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
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.
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.
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.
Java+
https://www.youtube.com/watch?v=eIrMbAQSU34
Master Java – a must-have language for software development, Android apps, and more! ☕️ This beginner-friendly course takes you from basics to real coding skills.
C+++
https://www.learncpp.com/
LearnCpp.com is a free website devoted to teaching you how to program in modern C++.
https://www.youtube.com/watch?v=ZzaPdXTrSb8
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具，在灵活性、可读性和性能上都具备相当的优势，近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
XGBoost+
[英文] What is XGBoost?
https://www.ibm.com/think/topics/xgboost
XGBoost (eXtreme Gradient Boosting) is a distributed, open-source machine learning library that uses gradient boosted decision trees, a supervised learning boosting algorithm that makes use of gradient descent.
https://www.youtube.com/watch?v=BJXt-WdeJJo
takes a deep dive into one of the most powerful machine learning algorithm, eXtreme Gradient Boosting, using a Jupyter notebook with Python.
DeepFM+
https://d2l.ai/chapter_recommender-systems/deepfm.html
DeepFM consists of an FM component and a deep component which are integrated in a parallel structure.
https://deeprs-tutorial.github.io/WWW_DNN.pdf
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.
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.
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
相关职位
社招技术
负责信贷业务营销算法体系搭建，主导用户拉新增长-转化-留存的全链路提效，设计兼顾短期ROI与长期LTV的复合指标，甚至引领团队方向发展。具体包括： 1、在业务的广告\推荐\营销等业务的算法域中持续优化，提升业务效果； 2、擅长推荐算法：优化推荐系统召回排序，内容理解与分类，兴趣意图识别等各个模块，提升点击、收藏、浏览时长多个目标； 3、擅长营销算法：在用户增长、因果推断、lookalike、广告竞价、智能文案\创意 等领域有了解和实践； 4、深入理解用户行为：有效理解互动反馈信息，结合数据挖掘，提升用户体验和转化率； 5、和上下游业务、产品、工程同学密切协作，推动产品的进化。
更新于 2025-08-27
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更新于 2025-08-27
社招3年以上技术类-算法
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。
更新于 2025-10-16
社招技术类-算法
1. 基于业务理解，通过数据科学的方法，挖掘业务增长机会点，具备良好的业务理解能力，具备数据挖掘、因果推断等算法能力； 2. 基于消费金融业务，针对花呗和分期等信贷业务，通过数学建模将业务问题抽象成算法问题，设计对应的算法解决方案，包括但不限于机器学习、运筹优化、控制等方法； 3. 算法研究和创新，能将前沿算法技术与业务场景相结合，沉淀创新的算法能力； 4. 结合业务场景探索大模型应用，包括但不限于智能服务对话、AIGC、LLM数据挖掘、LLM4Rec、Agent等方向；
更新于 2025-06-19