• Analyze and Assess Risk: Conduct in-depth analysis of financial data, transaction patterns, and user behavior to identify and assess potential credit, fraud, and compliance risks. • Develop and Implement Risk Controls: Partner with product, engineering and data science teams to design, build, and deploy automated risk controls and decisioning systems that balance user experience with platform safety. • Contribute to Strategic Planning: Provide valuable insights and recommendations to leadership on risk trends, policy improvements, and long-term risk strategy to support Found's growth and expansion. • Optimize Processes: Proactively identify areas for improvement in existing risk management processes and workflows, and drive initiatives to increase efficiency and scalability.

Role Description At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Data Annotation Specialists to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Own - Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. - Labeling elements of a piece of content rather than the content as a whole. - Assigning predefined categories or labels to items. - Evaluating the perceived quality and/or appropriateness of content. - Generating labels to advance understanding of a concept, trend etc. - Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. - Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. - Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. - Ordering or ranking items based on a set of preferences or criteria. - Creating prompts or questions that will be used to generate responses from a language model or other AI system. - Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). - Generating responses to prompts or questions using a language model or other AI system. - Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. - Producing concise summaries of longer pieces of text or data. - Converting spoken language or audio content into written text. - Converting text or spoken language from one language to another. - Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content. Qualifications - A High School Diploma or higher is required. - Professional or Expert level proficiency (C1/C2) in English. Requirements - The expected hourly salary range for this position is $9 p/hour, based on experience, skills, and qualifications. Company Description Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

• The Lead Data Scientist uses mathematics, statistics, modeling, business analysis, and technology to transform high volumes of complex data into advanced analytic solutions. • Works on problems of diverse scope and complexity ranging from moderate to substantial. • Develops, maintains, and collects structured and unstructured data sets for analysis and reporting. • Creates reports, projections, models, and presentations to support business strategy and tactics. • Advises executives to develop functional strategies (often segment specific) on matters of significance. • Exercises independent judgment and decision making on complex issues regarding job duties and related tasks and works under minimal supervision. • Designs predictive models and forecasting capabilities to estimate measure-level and contract-level Star Rating performance. • Drives the maintenance and enhancement of existing forecasting models, while also identifying opportunities for the team to advance what we deliver associated with Star Rating forecasts and make the connection to action simpler for teammates and leaders.

• Frame ambiguous marketing problems as well-scoped modeling and measurement challenges and own them end to end. • Develop and own attribution models that accurately allocate marketing investment across channels and touchpoints. • Build propensity, lead scoring, and churn models to prioritize where GTM teams should focus. • Model the relationship between product engagement signals and downstream commercial outcomes — expansion, retention, and conversion. • Design, execute, and interpret experiments (A/B, MVT, geo-based) with appropriate power analysis and statistical validity. • Build and apply segmentation models and cohort analyses to uncover behavioral patterns, lifecycle trends, and funnel opportunities. • Analyze organic search and AEO signals as modeling inputs to inform content strategy and improve discoverability. • Partner with analytics engineers to productionize models and move insights from notebook to pipeline. • Use AI tools to move faster, explore unfamiliar methods, and surface modeling options — while developing the judgment to know when AI-generated outputs are wrong or incomplete.