个人作者
Arockia Liborious
Data Science & Analytics Leader | Helping organizations rethink their data-driven decision making process | AI Storyteller
- Skills
- 494
- 仓库
- 1
- 职业覆盖
- 0
- 场景覆盖
- 0
- Stars
- ★ 7
Arockia Liborious 的 Skills 494
compliance-monitoring
Design compliance monitoring and testing programs that verify controls work in practice and surface issues before regulators do. Use when building monitoring plans, compliance testing, or continuous control monitoring.
complaints-handling
Operate complaints-handling processes that meet regulatory standards — logging, timely resolution, root-cause analysis, and fair outcomes. Use when designing or improving regulated complaints frameworks or analyzing complaint themes for conduct risk.
aml-kyc-basics
Apply AML/KYC fundamentals — customer due diligence, monitoring, suspicious activity escalation, and recordkeeping — for financial crime compliance. Use when designing onboarding controls, transaction monitoring concepts, or AML program components in financial services.
ai-model-compliance
Apply compliance controls to AI and automated decision systems — transparency, fairness, human oversight, documentation, and regulatory alignment. Use when deploying AI in regulated processes (credit, fraud, customer decisions) or responding to AI governance expectations.
data-quality-management
Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.
data-lineage
Capture and use data lineage — where data comes from, how it transforms, and what depends on it — for impact analysis, debugging, and trust. Use when tracing metric origins, assessing change impact, or diagnosing broken pipelines.
data-governance
Establish data governance — ownership, policies, decision rights, and accountability — so data is trusted, compliant, and usable across the organization. Use when designing governance operating models, assigning data owners, or fixing chronic trust and access problems.
data-ethics
Practice data ethics — privacy, fairness, consent, dual-use risk, and responsible communication of analytical findings. Use when working with sensitive data, people-affecting models, or high-impact analytics.
data-contracts
Define and enforce data contracts between producers and consumers — schema, semantics, SLAs, and compatibility rules — to prevent breaking changes. Use when scaling data products, reducing pipeline breakages, or formalizing producer accountability.
data-cleaning
Clean and prepare datasets — missing values, types, duplicates, outliers, and consistency — with documented, reversible transformations. Use when preparing data for analysis, modeling, or reporting pipelines.
data-catalog-and-discovery
Build and operate data catalogs so people can find, understand, and trust datasets — with metadata, ownership, and certification signals. Use when analysts cannot find the right table, or when enabling self-serve analytics at scale.
dashboard-design
Design dashboards that drive decisions — clear hierarchy, the right metrics, guided exploration, and trustworthy definitions. Use when building executive, operational, or self-serve analytical dashboards.
clustering-and-segmentation
Segment populations using clustering and related methods — with validation, interpretability, and actionable segment definitions. Use when discovering customer/user segments or grouping entities for differentiated treatment.
classification-basics
Build and evaluate classification models — class balance, metrics (precision/recall/ROC), thresholds, and error costs. Use when predicting categories such as churn, fraud, conversion, or risk tiers.
causal-inference-basics
Apply causal inference basics — potential outcomes mindset, confounding, and credible designs — when estimating effects from observational or experimental data. Use when asked “did X cause Y?” and simple correlation is not enough.
anomaly-detection
Detect anomalies in metrics and datasets — defining “normal,” reducing false alarms, and routing true issues to investigation. Use when building alerting on KPIs, fraud-like outliers, or data quality anomaly checks.
analytics-engineering
Practice analytics engineering — reliable, tested, versioned data transformations that turn raw sources into trustworthy analytical tables. Use when building dbt-style models, curated marts, or the semantic layer between raw data and BI.
ab-test-analysis
Analyze A/B tests end-to-end — data quality, SRM, metrics, segments, and decision recommendations with uncertainty. Use when reading experiment results and advising ship/iterate/kill decisions.