Data Scientist

Responsibilities About Data Cycling Center (DCC) AI is the new electricity and data is the energy for AI. Our core belief is that unstructured data contains the untapped wisdom of humanity — and by building the best AI-ready data infrastructure, we enable more meaningful, responsible, and creative uses of AI. Data Cycling Center (DCC) is a Data Science team that is responsible for building the mid-platform data layer that powers AI development across all business lines of TikTok, including e-commerce, advertising, livestreaming, and emerging technologies. Our initiatives include: Scaled human-in-the-loop (HITL) data processing (including capability, methodology, E2E process, platform), delivering the most cost-effective results at benchmark quality levels that outperform leading tech peers. Built a highly automated end-to-end data pipeline for AI-generated content (AIGC) that supports strong model performance with minimal resource input. Develop a comprehensive content understanding and insight generation system, setting new benchmarks for marketing intelligence in the online ads ecosystem. DCC aims to be the number 1 data service provider in the AI era. Our mission is to build the most effective (lossless and affordable) content understanding capabilities to fully satisfy AI needs of TikTok and to help the industry push the limit. Data Acquisition The Data Acquisition team is where raw human complexity meets machine reasoning. We\'re building systems that translate the messiness of human behavior — language, perception, decision-making — into structured, learnable intelligence for frontier AI models. Our job isn\'t to clean up data. It\'s to understand it — in all its ambiguity, context, and contradiction — and make it usable for models pushing the limits of what AI can do. We work on: Building novel data science solutions that evolve with human input Designing tooling that captures nuance across text, audio, video, and human feedback Creating robust signals from subjective and messy real-world data Scaling dynamic data operations to support multi-modal model training About the Role We are seeking a skilled Data Scientist to join our team, specializing in resource allocation and capacity planning. The ideal candidate will develop and implement advanced analytical models to optimize resource utilization, forecast demand, and enhance operational efficiency. This role requires a blend of statistical expertise, machine learning proficiency, and business acumen to drive impactful decisions. Responsibilities Optimisation Modelling: Design and develop data-driven models and frameworks that support intelligent assignment, prioritization, and resource utilization across operational workflows. Forecasting: Build and refine demand forecasting models to support workforce planning, inventory management, and operational scaling. Optimization: Design algorithms to optimize resource allocation, balancing cost, efficiency, and service quality. Feature Engineering: Build and maintain pipelines that transform raw signals such as behavioral data, labelling task attributes, and performance metrics into structured features for decision-making. Continuous Improvement: Monitor model performance, iterate on solutions, and incorporate feedback to enhance accuracy and impact. Project Planning and Execution: Develop and manage project plans, timelines, and budgets for data science initiatives. Stakeholder Management: Communicate project progress, challenges, and results to stakeholders and senior management. Qualifications Minimum Qualifications Bachelors degree in Data Science, Statistics, Computer Science, Operations Research, or a related field. At least 1 year of experience applying machine learning, statistical modeling, or optimization to operational or business challenges along with proven track record of building and deploying predictive models in a business environment. Experience in resource allocation, capacity planning, or operations research. Proficient in Python and SQL; experience working with large-scale or distributed data systems. Track record of owning end-to-end data projects, from requirements gathering to implementation. Excellent communication skills along with the ability to communicate technical insights clearly to both technical and business audiences. Preferred Qualifications Experience working within structured operational environments such as service delivery, content review, or quality control systems. Understanding of experimentation infrastructure, including A/B testing and metric design. #J-18808-Ljbffr