This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers the opportunity to safeguard platform integrity and protect users from fraud, abuse, and harmful behavior by leveraging advanced data science and analytics. You will design and implement robust data foundations, predictive models, and measurement frameworks that inform and shape safety strategies across the organization. - Analyze large-scale, complex datasets to detect fraud, abuse, and harmful behavior trends. - Design, build, and evaluate statistical models, heuristics, and machine learning systems for proactive fraud and abuse detection. - Develop predictive risk scoring, anomaly detection, and behavioral profiling systems. - Lead experimentation (A/B testing, causal inference) to assess product or policy changes and guide decision-making. - Define and track core safety metrics, dashboards, and reporting frameworks to monitor platform health and performance. - Collaborate cross-functionally to influence strategy, standardize analytical approaches, and ensure scalable data-driven decisions. - Communicate insights and recommendations effectively to technical and non-technical stakeholders. Qualifications - Bachelor’s or Master’s degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field. - 5+ years of experience in Data Science with large-scale data analysis; at least 1 year in fraud prevention, moderation, or risk. - Strong analytical and problem-solving skills with experience leading projects from concept to execution. - Proficiency in SQL and at least one scripting language (Python or R). - Hands-on experience with standard ML and statistical methods (prediction, classification, anomaly detection, time series). - Expertise in experimentation and causal inference to evaluate product or policy changes. - Strong cross-functional collaboration and influence skills without direct authority. - Excellent communication skills to translate technical concepts for non-technical stakeholders. Requirements - 2+ years in fraud prevention, moderation, or risk on a social platform. - Experience with anomaly detection and building/deploying ML models. - Experience working with text, image, or behavioral datasets. Benefits - Competitive base salary range of $175,000—$234,000 USD, with equity grants and potential for higher based on experience. - Comprehensive healthcare options and flexible benefits. - Retirement savings and income protection programs. - Paid time off, including flexible vacation and volunteer opportunities. - Inclusive, hybrid work environment with collaboration across multiple offices. - Professional development and recognition programs.

We are hiring a Data Scientist to join the Quality Analytics team within Global Data Insight and Analytics (GDI&A). This role focuses on delivering end‑to‑end machine learning and AI solutions that improve product quality, reliability, and customer outcomes. The ideal candidate has hands‑on experience applying machine learning, LLMs, and graph‑based approaches in production‑oriented environments. - Design and deliver end‑to‑end data science solutions, from problem formulation through modeling, evaluation, and deployment support. - Develop and evaluate machine learning models (e.g., classification, anomaly detection, forecasting, NLP) for quality and reliability use cases. - Build LLM‑enabled applications, including prompt‑based systems, RAG pipelines, and hybrid ML + LLM solutions. - Leverage Knowledge Graphs and graph‑based techniques to integrate structured and unstructured data. - Implement data and ML workflows on cloud platforms, preferably Google Cloud Platform (GCP). - Write high‑quality, reproducible, and maintainable code and collaborate with cross‑functional stakeholders. Required Qualifications - Master’s degree (required) in Data Science, Machine Learning, Statistics, Computer Science, Industrial Engineering, or related field. - 2+ years of experience delivering production‑relevant ML or advanced analytics projects. - 2+ years of experience using Python and common ML/DS libraries (e.g., scikit‑learn, PyTorch/TensorFlow, SQL). - 2+ years of hands‑on experience in machine learning model development, experimentation, and evaluation. - 2+ years of experience applying or integrating Large Language Models (LLMs) into real analytics workflows. - 2+ years of experience working with cloud‑based data and ML platforms, preferably GCP. Preferred Qualifications - PhD in Data Science, Machine Learning, Statistics, Computer Science, Industrial Engineering, or related field. - Experience with Knowledge Graphs, graph databases, or graph‑augmented ML/LLM systems. - Familiarity with RAG architectures, vector search, or hybrid retrieval systems. - Experience in quality, warranty, manufacturing, or industrial analytics. - Exposure to MLOps practices (CI/CD, model monitoring, retraining). You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply! As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder…or all of the above? No matter what you choose, we offer a work life that works for you, including: • Immediate medical, dental, and prescription drug coverage • Flexible family care, parental leave, new parent ramp-up programs, subsidized back-up child care and more • Vehicle discount program for employees and family members, and management leases • Tuition assistance • Established and active employee resource groups • Paid time off for individual and team community service • A generous schedule of paid holidays, including the week between Christmas and New Year’s Day • Paid time off and the option to purchase additional vacation time. For a detailed look at our benefits, click here: Benefit Summary This role is remote but if you live within 50 miles of a Ford Hub (Dearborn, MI or Palo Alto), you will be required on-site 4x a week. *Visa Sponsorship IS provided for this specific role* *Relocation assistance IS provided for this specific role* Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, If you need a reasonable accommodation for the online application process due to a disability, please call 1-888-336-0660. #LI-Remote #LI-DS2 GSR7

• Lead the design, development, and deployment of advanced ML models and predictive algorithms to solve critical business problems, such as churn prediction, user segmentation, and product usage forecasting. • Serve as a technical subject matter expert, guiding the team on best practices in statistical analysis, experimentation design, and code quality. • Partner with PMs and Engineers to translate ambiguous business questions into rigorous data science projects with actionable outcomes. • Drive the adoption of MLOps best practices, ensuring models are scalable, reproducible, and monitored effectively in production. • Perform deep-dive analyses on user behavior and product performance to identify opportunities for optimization and growth. • Mentor and coach other data scientists and analysts, fostering a culture of continuous technical learning and innovation. • Communicate complex technical findings to non-technical stakeholders through compelling data storytelling and visualization.

We are hiring a Data Scientist to join the Quality Analytics team within Global Data Insight and Analytics (GDI&A). This role focuses on delivering end‑to‑end machine learning and AI solutions that improve product quality, reliability, and customer outcomes. The ideal candidate has hands‑on experience applying machine learning, LLMs, and graph‑based approaches in production‑oriented environments.