- Company Name
- Sardine
- Job Title
- Head of Data Science & POCs
- Job Description
-
Job Title: Head of Data Science & POCs
Role Summary: Senior leader responsible for building and scaling real‑time fraud detection models, driving high‑impact proof‑of‑concept and proof‑of‑value initiatives for enterprise customers, and leading a cross‑functional team of fraud analysts and data scientists to deliver measurable reduction in fraud losses and customer ROI.
Expactations: 10+ years in fraud analytics, data science, or machine learning with at least 2 years leading technical teams that ship real‑time risk solutions in high‑growth payments, financial, or fintech environments; proven track record of reducing fraud losses, false positives, and managing POC/POV workflows that achieve quantifiable customer outcomes; strong applied ML, anomaly detection, experimentation, and advanced SQL expertise; ability to communicate complex insights to both technical and non‑technical stakeholders.
Key Responsibilities: • Lead end‑to‑end POCs and POVs to demonstrate fraud‑loss reduction and ROI for prospective customers. • Design, build, optimize, and scale real‑time decision engines combining ML models, features, and rules across billions of events. • Hire, coach, and mentor a team of fraud analysts and data scientists. • Implement experimentation frameworks (A/B tests, bandit models) to validate new strategies safely. • Collaborate with Product, Engineering, and Commercial teams to embed insights into dashboards, APIs, and pricing strategies. • Present findings and strategies to customers, executives, and external stakeholders. • Drive roadmap and strategy for new signals, feature builds, and feedback loops to improve platform intelligence and speed.
Required Skills: • Leadership and team building in a data‑science context. • Deep knowledge of machine learning techniques for fraud detection (anomaly detection, supervised & unsupervised models). • Experimentation design and statistical analysis (A/B tests, bandit methodologies). • Advanced SQL and familiarity with modern data infrastructure (Kafka, Flink, feature stores, vector databases). • Strong communication, executive presence, and storytelling ability. • Experience with real‑time streaming analytics and risk engine deployment.
Required Education & Certifications: • Bachelor’s or master’s degree in Computer Science, Statistics, Mathematics, or related field (preferred). • Relevant certifications in data science, machine learning, or fraud analytics are a plus.