128 applicants

Company

Original Job Post

TSMC · 5 days ago

Senior Data Scientist

San Jose, CA

Full-time

Hybrid

Senior Level

$170K/yr - $270K/yr

7+ years exp

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Consumer ElectronicsElectronics

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Responsibilities

Create and manage advanced machine learning, deep learning, and Large Language Model (LLM) algorithms to develop data-driven solutions for complex business problems

Analyze and interpret complicated data sets to provide actionable insights for enhancing decision-making processes

Work with cross-functional teams to identify and prioritize data-driven opportunities, including developing predictive models

Lead teams of junior data scientists and data engineers to develop efficient procedures and provide implementation guidance

Assess and refine model performance to ensure precision and dependability

Communicate insights and recommendations to technical and non-technical stakeholders

Design and implement AI, machine learning, and data science tools and techniques to enhance existing processes

Produce publications in leading AI conferences and/or journals

Qualification

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AIMachine LearningData ScienceRequirements GatheringData WranglingPre-processingClassical ML AlgorithmsSupervised LearningUnsupervised LearningDeep LearningNetwork ScienceText AnalyticsNamed Entity RecognitionSentiment AnalysisTopic ModellingTime Series ModelingForecastingLLM-based ApproachesPrompt EngineeringReinforcement LearningEnd-to-end ML DevelopmentUser Interface DeploymentTeam ManagementText ProcessingMulti-lingual AnalysisGraph EmbeddingNeural Information RetrievalAdversarial LearningKnowledge DistillationSelf-supervised Learning

Required

Ph.D. in computer science, information systems, information science, statistics, or related AI or machine learning field

At least 7-10 years of significant AI, machine learning, and data science-related working and research experience

Experience gathering requirements from business stakeholders and developing technical machine learning designs for implementation

Strong practical experience with data wrangling, pre-processing, and extraction from structured and unstructured data sources

Strong conceptual and practical knowledge and experience of classical machine learning algorithms and learning paradigms, including supervised learning and unsupervised learning

Strong skills in deep learning implementation, including data encodings, processing units, and learning paradigms

Proficiency in developing novel machine learning or deep learning algorithms based on unique dataset characteristics and business requirements

Strong practical network science skills and experience

Experience with text analytics for named entity recognition, sentiment analysis, and topic modelling

Experience with time series modeling and forecasting with statistical and/or deep learning-based approaches

Experience fine-tuning LLM-based approaches with strategies such as low rank adaptation, few shot learning, and others

Experience with prompt engineering on LLMs with techniques such as reinforcement learning, prompt tuning, and others

Knowledge about deploying machine learning models into user interfaces

Publications in leading AI conferences (e.g., NeurIPS, ICLR, ICML, KDD) and/or journals (e.g., IEEE TKDE, ACM TOIS)

Demonstrated experience leading teams of junior data scientists and engineers in end-to-end machine learning development and deployment

Experiences in iteratively developing machine learning designs based on end user feedback

Must be willing to travel to Taiwan for at least 3 months each year for training, team building, and project coordination

Preferred

Knowledge on processing text in financial, accounting, and market analysis reports

Multi-lingual text analysis using packages like Stanza, Polyglot, or Textflint

Knowledge of graph embedding techniques such as graph convolutional networks and graph attention networks using packages such as stellargraph, PyG, or Deep Graph Library

Understanding of neural information retrieval approaches, including deep structured semantic models, entity resolution techniques, and retrieval augmented generation (RAG)

Familiarity and practical experience with learning paradigms such as adversarial learning, knowledge distillation, and self-supervised learning