DataVisor

Full-stack Engineer

• Own the technical direction of the real-time detection platform, including streaming, storage, and the training pipelines that support it
• Translate product and engineering roadmaps into clear technical plans, milestones, and execution priorities
• Proactively identify technical risks, dependencies, and trade-offs before they impact delivery
• Lead design and architecture reviews, make technical trade-off decisions, and document the reasoning behind key decisions
• Stay hands-on by coding, reviewing code, debugging issues, and supporting the team during production incidents
• Mentor engineers and raise the bar for system design, code quality, operational excellence, and technical execution
• Own the operational health of the platform, including alert quality, on-call load, incident follow-through, and root-cause prevention
• Partner directly with Product, TAM, and customer-facing teams on customer-impacting issues, ensuring clear impact assessment, prioritization, ownership, and next steps
• Define how the team uses AI agents and AI-assisted tools in engineering workflows, including verification standards and safe usage practices
• Build, evaluate, and improve LLM- and agent-assisted tools for engineering and operations use cases, such as triage, root-cause analysis, alert summarization, and evaluation harnesses

Tech Lead – Software Engineering

Account Manager

• Build, manage, and grow strategic partnerships across reseller, referral, marketplace, core banking, digital banking, payments, and fintech ecosystems.
• Own partner-sourced and partner-influenced pipeline and ARR targets.
• Develop repeatable partner enablement motions, including account mapping, pipeline reviews, sales training, co-selling processes, and joint GTM plans.
• Work directly with partners and internal Sales teams to identify, qualify, progress, and close partner-led opportunities.
• Translate DataVisor’s fraud, AML, risk, and AI platform capabilities into clear joint value propositions for partners and end customers.
• Partner cross-functionally with Sales, Product, Solutions Engineering, Marketing, Legal, and Customer Success to support partnership execution.
• Track and report on partner pipeline, win rates, time-to-first-deal, partner activation, and revenue contribution.
• Help build the operating cadence and playbooks needed to scale DataVisor’s partner ecosystem.

Director, Strategic Partnerships – Channel Sales

Data Scientist

• Lead the full lifecycle of fraud detection features and models, from ideation and data exploration to prototyping, productionizing, and monitoring.
• Develop predictive features from large-scale, multi-dimensional data, including user behavior, device intelligence, network graphs, and transaction records.
• Process massive, noisy, and imbalanced datasets using Spark, SQL, and the proprietary AI platform.
• Leverage agentic AI to automate analytic pipelines and develop reusable tools for fraud investigation, feature generation, and reporting.
• Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces.
• Reconstruct attacker sequences and hypothesize actor intent and tooling.
• Produce evidence-backed technical reports and case studies for product, engineering, operations, legal, and executive stakeholders.
• Generate customer-facing fraud trend reports by synthesizing case-level findings and aggregate data.

Senior Data Scientist – Fraud Detection

• Own the technical direction of the real-time detection platform, including streaming, storage, and training pipelines
• Translate product and engineering roadmaps into technical plans, milestones, and execution priorities
• Identify technical risks, dependencies, and trade-offs before they impact delivery
• Lead design and architecture reviews, make technical trade-off decisions, and document key decisions
• Code, review code, debug issues, and support the team during production incidents
• Mentor engineers and raise standards for system design, code quality, operational excellence, and technical execution
• Own platform operational health, including alert quality, on-call load, incident follow-through, and root-cause prevention
• Partner with Product, TAM, and customer-facing teams on customer-impacting issues
• Define team standards for AI agents and AI-assisted engineering workflows, including verification and safe usage practices
• Build, evaluate, and improve LLM- and agent-assisted tools for triage, root-cause analysis, alert summarization, and evaluation harnesses