\\\\About the Role\\\\ The Trusted Identity Applied Science team (IDML) builds ML models and GenAI solutions to detect and mitigate identity fraud on Uber platform and across all LoBs. Part of Uber Core Services organization, the team is focused on building large-scale modeling solutions to make sure only legitimate, verified and authorized users can access Uber products and services. As a member of a concentrated team of ML model developers, you will play an influential role in building solutions in a highly cross-functional and collaborative environment and help make our platform as safe as possible for all users. \\\\What the Candidate Will Need / Bonus Points\\\\ \-\-\-\- What the Candidate Will Do ---- 1\\. Design and deploy a diverse suite of ML, Deep Learning, NLP models, and GenAI to detect and mitigate platform abuse, ensuring a secure environment for all users. 2\\. Leverage a broad toolkit of supervised and unsupervised techniques, including time-series forecasting and anomaly detection, to identify emerging threat vectors. 3\\. Conduct rigorous offline evaluations and online A/B testing, utilizing causal inference to balance high-precision fraud prevention with a seamless user experience. 4\\. Take full ownership of the model lifecycle, moving from initial prototype to 0 to 1 production deployment in close collaboration with engineering teams. 5\\. Architect and build sophisticated internal data tools to automate manual detection tasks and empower analysts with real-time anomaly detection capabilities. 6\\. Partner with a multidisciplinary team of Product Managers, Data Analysts, and Software Engineers to translate complex findings into actionable product strategies. \-\-\-\- Basic Qualifications ---- 1\\. Masters or PhD in Computer Science, Machine Learning, Statistics, Operations Research, or a related quantitative field. 2\\. Deep theoretical knowledge of statistics, linear algebra, optimization, and the foundations of Generative AI. 3\\. Exceptional analytical skills with a proven ability to translate complex business problems into technical ML solutions. 4\\. Expert-level knowledge in at least two of the following: Deep Learning, ML System Design, Generative AI, A/B Testing/Experimentation, or Causal Inference. 5\\. Proficiency in building and deploying models using PyTorch or TensorFlow. 6\\. Mastery of Python or R for data science and model development. 7\\. Proficiency with SQL for data extraction and manipulation. 8\\. Familiarity with compiled languages such as Go or Java is a plus. \-\-\-\- Preferred Qualifications ---- 1\\. A track record of high-level contribution, evidenced by either publications in top-tier conferences (e.g., NeurIPS, ICML, CVPR) or a portfolio of successful production-grade ML deployments. 2\\. Hands-on experience with large-scale distributed training and Big Data ecosystems e.g. Spark or Flink. 3\\. A researcher-practitioner mindset-the ability to deep-dive into complex problems via EDA and statistical analysis, moving independently from initial theory to functional prototype and final production. 4\\. An owner's mindset with the ability to communicate technical trade-offs effectively to both engineering and business stakeholders. 5\\. Past experience in building models in risk and fraud domains is a plus. 6\\. Experience with Agentic AI is a plus. For San Francisco, CA-based roles: The base salary range for this role is USD$161,000 per year - USD$179,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$161,000 per year - USD$179,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form-

Candidates should possess a Masters or PhD in a quantitative field such as Computer Science, Machine Learning, Statistics, or Operations Research. A deep theoretical understanding of statistics, linear algebra, optimization, and the foundations of Generative AI is required. The role demands exceptional analytical skills to translate complex business problems into technical ML solutions. Expertise in at least two of the following areas is necessary: Deep Learning, ML System Design, Generative AI, A/B Testing/Experimentation, or Causal Inference. Proficiency in building and deploying models using PyTorch or TensorFlow, and mastery of Python or R for data science are essential. Familiarity with SQL for data extraction and manipulation is also required.