EverC

Description About the Role EverC, now part of G2 Risk Solutions, is a pioneer in its field. By using technologies that illuminate the darkest corners of the internet and provide unparalleled visibility into the largest source of data in the world, the internet. EverC uses artificial intelligence and machine learning techniques to assess hundreds of millions of domains and effectively categorize the internet. These insights shine a light on hidden relationships and risks, identify an entity’s full digital fingerprint, and reveal new opportunities to scale businesses efficiently and confidently. Your opportunity: We are looking for a passionate software engineer to help us build the foundation for storing and serving the data of the company's product of the future. As a Software Engineer you’ll be expected to: • Be fully hands-on, lead your parts from A-Z (why, what, how), continuously raise the bar. • Drive the engineering efforts in a cross-functional squad. Your day-to-day: • Design, develop and influence how we process data at scale, together with amazing people. • Solve diverse complex problems of scale, performance and business logic. • Work in an agile development methodology. • Review code, architecture and data to identify and troubleshoot problems and areas for improvement and optimization. • Share knowledge and mentor other engineers. Requirements • Expertise in Java or Python with at least 5+ years of experience in backend development of streaming application at scale. • Experience with large-scale distributed systems in production. • Experience with microservices-based architecture. • Experience designing and implementing large-scale apps in production • Proficiency in Kubernetes & Kafka (or equivalent streaming enabler). • Hands-on experience with cloud platforms, preferably AWS. • Good Communication skills. • Team player, hard worker, quick learner, independent and loves challenges. • Fluent in English. • BSC in Computer Science or equivalent. Preferred qualifications: • Experience with Spark, Scala, Airflow • Experience with columnar storage formats and OLAP systems for optimized analytical querying and data storage • Experience with CI/CD systems (Git Actions, ArgoCD). • Experience with infrastructure tools such as Cloud Formation and Terraform. • Experience with agentic tools.

Description EverC, now part of G2 Risk Solutions, is a pioneer in its field. By leveraging technologies that illuminate the darkest corners of the internet and provide unparalleled visibility into the largest source of data in the world, EverC uses artificial intelligence and machine learning techniques to assess hundreds of millions of domains and effectively categorize the internet. These insights reveal hidden relationships and risks, identify an entity’s full digital fingerprint, and create new opportunities for businesses to scale efficiently and confidently. About the Role We’re looking for a hands-on Data Scientist who owns AI and LLM-powered products end to end — and cares about how they perform for real users. You will own the modeling and evaluation of our fraud and risk detection products: exploring the data, designing models and prompts, evaluating them offline, and monitoring them in production. You’ll work side by side with engineers to ship your models into large-scale, real-time systems — you drive the data science and the product outcomes, while engineering owns the platform plumbing. This is a data science role at heart. You won’t be a data engineer, but you’ll be comfortable working close to the data and the systems, and knowing your way around modern ML and data infrastructure is a strong plus. Responsibilities Data Science & Modeling • Own the design, build, and evaluation of LLM-based and classical ML models for classification, entity extraction, and risk scoring on large-scale, real-world data. • Lead prompt design and iteration — model configurations, fallback strategies, and tradeoffs between cost, latency, and quality. • Apply sound statistical judgment on adversarial, noisy, or imperfectly labeled data. • Drive the modeling decisions behind how LLMs and ML models fit into streaming, event-driven production systems. Product & Platform Partnership • Partner with engineers to ship your models into real-time streaming and large-scale batch pipelines — you own the modeling, they own the platform. • Shape internal evaluation tooling, LLM observability, and model-performance monitoring. • Define the requirements your infrastructure must meet to support rapid experimentation without hurting production reliability. Data Quality & Product Performance • Own the data-quality and product metrics that matter (precision, recall, coverage, latency, cost efficiency). • Build measurement frameworks for production systems and offline experiments. • Analyze production data to find labeling gaps, false-positive patterns, and new detection opportunities. • Drive experimentation (A/B tests, shadow deployments, offline evaluation) and back every decision with measurable business outcomes. Requirements • 5+ years applied data science / ML, including production model deployment. • Hands-on LLM prompt engineering and evaluation (not just fine-tuning theory). • Strong Python. • Experience with large-scale data processing concepts and technologies. • Strong grasp of data-intensive applications and modern data architectures. • Experience building cloud-native systems (AWS preferred). • Excellent communication and stakeholder management. • Comfortable with ambiguous, adversarial data and designing labels/heuristics where ground truth is imperfect. Preferred Qualifications A data scientist who is also fluent in the ML and data-engineering stack is a big plus: • Data & Infrastructure: Apache Spark, Kafka, Kubernetes, Docker, EMR, Airflow, Iceberg / Delta Lake, K8s • Cloud & DevOps: Infrastructure as Code (Terraform), CI/CD (GitHub Actions), monitoring & observability platforms • AI & ML: ML platforms, LLM-powered applications, vector databases