Senior Specialist - Data Sciences

Required Qualifications

• Strong software engineering fundamentals and proficiency in Python, Java, Go, Type Script are a strong plus
• Experience working with Codex
• Proven experience building LLM powered applications in production with tool calling function, calling structured outputs, retrieval and evaluation
• Experience designing distributed systems and APIs (REST, RPC) plus event‑driven patterns (Kafka, SQS, Pub/Sub)
• Solid understanding of data engineering basics: SQL, data modeling, feature engineering, and data quality
• Hands‑on knowledge of cloud platforms (AWS, Azure, GCP), containers, Docker, and orchestration (Kubernetes) preferred
• Ability to write clean, testable, secure code; comfortable with code reviews and engineering rigor
• Experience with multi‑agent systems, planning, verification, and autonomous workflow execution
• Experience with vector databases, hybrid search, and knowledge graphs
• Familiarity with model evaluation (offline evals, golden datasets), adversarial testing, regression harnesses, and A/B testing

• Agent frameworks:
  Lang Graph, Semantic Kernel, similar orchestration frameworks, or equivalent custom implementations
• RAG tooling: embedding pipelines, hybrid retrieval, reranking, chunking strategies, citation provenance
• Observability:
  Open Telemetry, structured logging, dashboards
• Data systems: OLTP, analytics warehouses, lakes, streaming pipelines, feature stores (optional)
• Testing: unit and integration tests, replay tests for agent traces, evaluation harnesses for LLM outputs

Design and implement agent architectures, planners, executors, and tools using agents, multi‑agent orchestration, reflection, and evaluation loops. Build tooling integrations for agents with merchant systems, underwriting platforms, transaction stores, risk engines, CRM, case tools, knowledge bases, and workflow engines. Implement robust state management, session memory, task plans, provenance, traceability, and replay ability of agent actions.

Develop RAG pipelines over policies, SOPs, card network rules, underwriting guidelines, dispute playbooks, and merchant agreements. Apply prompt and system design, structured output patterns, and schema validation for deterministic agent behavior. Optimize for latency, cost, and reliability using caching, model routing, and evaluation‑driven prompt iteration. Combine LLM agents with classical ML models (fraud scoring, anomaly detection, risk scoring, and rules engines).

Build feedback loops from outcomes (chargeback win rate, false positives, approval uplift) to continuously improve models and agent strategies.

Combine LLM agents with classical ML models (fraud scoring, anomaly detection, risk scoring, rules engines). Build feedback loops from outcomes to continuously improve models and agent strategies.

Implement guardrails, PII handling, policy enforcement, prompt injection defenses, tool‑based rate limiting, and safe fail‑over. Ensure auditability of agent actions, evidence used, and human approval where required (human‑in‑the‑loop). Build CI/CD for agent services, evaluation suites, telemetry, drift detection, and incident response playbooks. Instrument agent behavior using tracing spans, structured logs, and metrics (task success, tool errors, hallucination indicators).

Build CI/CD for agent services, evaluation suites, telemetry, drift detection, and incident response playbooks. Instrument agent behavior using tracing spans, structured logs, and metrics.

Partner with Product, Risk, Ops, Underwriting, Compliance, and Engineering to convert business problems into deployable AI solutions. Mentor engineers, set standards for agent design patterns, testing, and production readiness.

• Comprehensive Medical Plan (Medical, Dental, Vision)
• Short‑Term and Long‑Term Disability Coverage
• 401(k) plan with company match
• Life Insurance
• Vacation time, sick leave, paid holidays
• Paid paternity and maternity leave

The…