G
Senior Data Scientist
On-siteFull-timeData ScienceEnterprise SoftwareData Infrastructure
Own AI- and LLM-powered fraud and risk detection products from modeling and evaluation through production monitoring. Partner with engineering teams to deploy models in real-time and batch systems and improve measurable product outcomes.
Responsibilities
- Design, build, and evaluate LLM-based and classical machine learning models for classification, entity extraction, and risk scoring.
- Develop and iterate prompts, model configurations, fallback strategies, and cost, latency, and quality tradeoffs.
- Apply statistical judgment to noisy, adversarial, and imperfectly labeled data.
- Define how models operate within streaming, event-driven production systems.
- Partner with engineers to deploy models in real-time streaming and large-scale batch pipelines.
- Shape evaluation tooling, LLM observability, and model-performance monitoring.
- Define infrastructure requirements that support rapid experimentation and production reliability.
- Own data-quality and product metrics including precision, recall, coverage, latency, and cost efficiency.
- Build measurement frameworks for offline experiments and production systems.
- Analyze production data to identify labeling gaps, false positives, and new detection opportunities.
- Lead A/B tests, shadow deployments, and offline evaluations based on measurable business outcomes.
Requirements
- At least 5 years of applied data science or machine learning experience, including production model deployment.
- Hands-on experience with LLM prompt engineering and evaluation.
- Strong Python skills.
- Experience with large-scale data processing and modern data architectures.
- Experience building cloud-native systems; AWS experience is preferred in the source posting.
- Ability to work with ambiguous, adversarial, and imperfectly labeled data, including label and heuristic design.
- Strong communication and stakeholder-management skills.
Nice to have
- Experience with Apache Spark, Kafka, Kubernetes, Docker, EMR, Airflow, Iceberg or Delta Lake.
- Experience with Terraform, CI/CD, monitoring, and observability platforms.
- Familiarity with ML platforms, LLM applications, vector databases, AI evaluation frameworks, or MLOps.