Data Scientist (RiskOS)

Join our RiskOS team as a Data Scientist focused on workforce verification and hiring fraud. You will own the end-to-end data science lifecycle, analyze multi-source data to uncover fraud patterns, and collaborate with product and engineering teams. This role is ideal for data scientists with strong fraud or risk experience who enjoy working with unstructured text and are comfortable with data engineering and productionization.

• Posséder l'ensemble du cycle de vie de la science des données pour les cas d'utilisation de vérification de la main-d'œuvre sur RiskOS, de l'exploration des données au déploiement.
• Explorer et analyser les sources de données liées à la main-d'œuvre pour identifier les schémas de fraude, concevoir et mettre en œuvre des règles et des modèles d'apprentissage automatique.
• Collaborer avec les équipes produit et d'ingénierie pour la production des modèles, des règles et des composants GenAI au sein de RiskOS.

• Comfort working with large, messy, and heterogeneous datasets (JSON workflows, logs, event streams, third-party enrichments) and building reusable abstractions or utilities to make them usable for yourself and others
• Strong analytical and problem-solving skills, including comfort reasoning about ambiguous signals and adversarial behavior in fraud or workforce contexts
• Exposure to Natural Language Processing and/or unstructured text analytics—such as resume or document parsing, entity extraction, similarity search, or basic embedding-based methods—ideally applied in real-world products
• Experience owning end-to-end analytics and/or model development projects: problem framing, data wrangling, feature engineering, model training, evaluation, and deployment support
• Some hands-on experience working with Generative AI or LLM-based products (e.g., using commercial LLM APIs, prompt design, RAG-style retrieval, or evaluation of LLM outputs), with an interest in deepening this skill set
• Clear, concise communication skills and the ability to explain complex analyses, models, and GenAI behavior to non-technical stakeholders (product, GTM, customers)
• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience
• Strong proficiency in Python and SQL, including experience with common data science and ML libraries (e.g., pandas, scikit-learn, XGBoost, PySpark, or similar)
• 3–6 years of hands-on experience in data science, machine learning, or applied analytics, with meaningful work on fraud, risk, trust & safety, or workforce/hiring analytics preferred
• Ability and willingness to take on light data engineering and production-oriented tasks when needed (e.g., building ETL transforms, contributing to Airflow/Spark jobs, or instrumenting basic monitoring) in partnership with engineering
• A bias toward ownership, learning, and collaboration—comfortable working in a fast-paced, evolving environment, receiving guidance from senior data scientists while steadily increasing your own scope and autonomy
• Direct experience with workforce, HR tech, ATS/HRIS data, or hiring funnel analytics
• Prior work on identity verification, device intelligence, or orchestration/rules engines (e.g., RiskOS or similar systems)
• Familiarity with evaluation and monitoring of GenAI systems (e.g., offline benchmarks, human-in-the-loop review, safety/hallucination checks)