Responsibilities
About the Team
PDPO(Privacy and Data Protection Office) is the organization to lead, supervise, and empower all TikTok's privacy work in an accountable and industry leading way. This team is the expert in the landscape of privacy risks and passionate about consulting across the company on implementing the proper safeguards and technical mitigations to ensure that our users' privacy is honored across the TikTok's products and platforms.
About the role
The Machine Learning Scientist will lead and collaborate with cross-functional teams to design, develop, and deploy sophisticated machine learning algorithms to enhance the performance of our business systems.
- Formulate end-to-end machine learning models by utilizing LLM, NLP techniques to deal with real-world signals generated from privacy products/ incidents/ review areas.
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- Design and implement a reasonable offline data architecture for large-scale data systems
- Analyze extensive, complex datasets to extract meaningful insights, identify opportunities for improvement, and facilitate data-driven decision-making.
- Design and execute experiments, testing and iterating on machine learning models to optimize recommendation functions and boost user satisfaction.
- Communicate final recommendations and drive decision making.
You will utilize data-driven techniques in building solutions to help measure, validate, identify, and envision TikTok's privacy evolution. You will design and scope out projects, build operationalized analytics tools and platforms, and most importantly, use data and AI models to find insights, diagnose problems and tell compelling stories.
Besides the technical side, you will also get the opportunity to work closely with various XFN teams, including but not limited to engineering, legal, audit, security, public, government relations, and products. You will be expected to build deep domain knowledge and build a strong domain expertise in privacy.
Qualifications
Minimum Qualifications:
- Requires a Bachelor's degree in Finance, Mathematics, Statistics, Operations Research or other related field and two years of experience in the job offered or in a machine learning-related role.
- Solid background in NLP and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods.
- Experience performing data extraction, cleaning, analysis and presentation for medium to large datasets
- Experience with SQL, ETL, data modeling, and at least one programming language (e.g., Python, C++, C#, Scala, etc.)
- Experience with scientific computing and analysis packages such as NumPy, SciPy, Pandas, Scikit-learn, dplyr, or ggplot2
- Experience with statistics methods such as forecasting, time series, hypothesis testing, classification, clustering or regression analysis
- Experience with data visualization libraries such as Matplotlib, Pyplot, ggplot2
Preferred Qualifications:
- Experience with machine learning libraries and deep learning toolkits such as PyTorch, Caffe2, TensorFlow, Keras or Theano.
- Experience with Large Language Model is a plus.
- Familiar with many open source frameworks in the field of big data, e.g.Hadoop, Hive,Flink, FlinkSQL,Spark, Kafka, HBase, Redis, RocksDB, ElasticSearch etc is a plus.
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