Data Analyst Engineer (AI)

Job Description:

We are hiring a core partner to co-own our next-gen anomaly detection & attribution (ADA) platform that powers real-time monitoring, automated diagnosis, and decision support across multiple games/regions. You will lead end-to-end work—architecture, development, maintenance, and analytical impact—while advancing our LLM-Agent capabilities to turn noisy signals into clear, actionable narratives for business teams.

What You’ll Do

●Own the ADA platform lifecycle: design, implement, and maintain robust pipelines for T+1/near-real-time anomaly detection, multi-baseline benchmarking (global/region/country), and multi-source attribution (holidays, versions, events, migrations, user behavior).

●Advance detection & inference: productionize change-point/outlier methods, time-series features, causal/ablation checks, and automated “storyline” generation that explains what happened, why, and what to do next.

●Build AI Agents around the system: design tool-use and reasoning flows (ReAct/LangGraph or similar) to enable conversational drill-downs for ops/PMs.

●Productize insights: ship dashboards/alerts (email/Chat/WeCom) and concise decision memos; iterate with stakeholders in publishing, ops, marketing, and analytics.

●Collaboration: partner with DS/Eng/PM to scope, roadmap, and ship; document architecture, APIs, and runbooks.

Job Requirements:

Must-Have Qualifications:

●Technical core:

○Strong Python engineering (clean code, testing, packaging); solid SQL for large analytical workloads.

○Hands-on with time-series/anomaly detection (change-point, robust stats, seasonality/holiday adjustment, multivariate signals) and attribution logic.

○Practical exposure to LLM application patterns (tool calling, function calling, retrieval/RAG, Agent planning), and at least one framework/API (OpenAI/Claude/DeepSeek, LangChain/LangGraph, etc.).

●Systems/product mindset: ability to translate business pain points into measurable detection/attribution logic and ship reliable features on short cycles.

●Ownership & reliability: you build guardrails, monitors, and docs; you debug in production and prevent regressions.

Nice-to-Have / Preferred:

●Model eval & prompt engineering: rubric design, offline eval sets, golden tasks, prompt/test versioning, data flywheels.

●Causal & experimentation: diff-in-diff, CUPED, synthetic controls, online AB testing at scale.

●Dashboards & alerts: Superset/Tableau/Looker/Metabase; alerting via Slack/WeCom/Email with noise-reduction heuristics.

●Gaming analytics domain: retention funnels, reactivation, event/version rollout attribution,fraud/smurf detection.

●Infra & ops: Docker/K8s, CI/CD, IaC; cloud stacks (GCP/AWS/Azure); cost/perf tuning