Data Analyst

Overview

At Cybernetic Controls Limited (CCL), we are committed to global leadership in providing innovative digital solutions that empower businesses to reach their full potential. As a remote-first company, we believe in empowering our employees to work in a way that best suits their individual needs, fostering a culture of flexibility and trust. Since our founding in 2020, we have successfully delivered high-quality resources to our clients in the FinTech sector across various business areas. Read more on the Cybernetic Controls website.

Our Client | Single Rulebook

Single Rulebook is an AI‑powered regulatory intelligence platform for financial services. It automatically identifies, categorises, and maps regulatory changes across jurisdictions so compliance teams see only what's relevant to their business, not a flood of noise. By filtering out up to 80% of irrelevant updates and maintaining a full audit trail from signal to response, the platform helps firms stay ahead of change while reducing operational risk and manual effort.

The product brings together primary regulatory texts, technical standards, guidance, and exchange rulebooks and applies AI across the pipeline: intelligent classification to a domain taxonomy, change detection and significance scoring, semantic search and cross‑border rule mapping. Interactive rule maps and contextual connections show the ripple effects of changes across business lines and jurisdictions, while delivery management tools drive timely, accountable action and evidence for regulators

Regulatory and exchange rules change all the time. When important updates slip through, firms waste weeks on rework and carry real risk. Single Rulebook cuts the noise so teams see only the few changes that actually affect their products and venues, shows exactly what changed and why it matters, connects related obligations across markets, and records how you responded. The result is fewer surprises, faster decisions, and confident answers when regulators or senior management ask, "What did we do about this?"

Single Rulebook is a Kaizen's company. Kaizen is a leading RegTech firm that helps financial institutions meet complex reporting and compliance obligations with accuracy and assurance. Founded by industry experts, Kaizen delivers quality assurance solutions for EMIR, MiFIR and SFTR transaction reporting, alongside regulatory intelligence platforms such as Single Rulebook. Their mission is to reduce regulatory risk and operational burden through automated, data‑driven tools that improve accuracy, transparency and auditability. Kaizen's clients include major banks and asset managers globally, who rely on the company's blend of advanced technology and deep regulatory expertise to keep pace with an ever‑changing regulatory landscape.

Job Summary:

You will be the first line of quality for our regulatory data, with an initial focus on QA and review of external ML outputs from our primary vendor. You'll investigate correlations and behaviours across large datasets, form hypotheses about data quality and user impact, validate with data tooling, and drive issues through a structured rectification workflow with both internal teams and vendors. You'll also help design and scale our own data review methodologies, combining human-in-the-loop (HITL) review with bulk/automated checks.

You will operate in two modes: proactive sweeps across our datasets and rapid response to internal and external data quality queries—turning ambiguous "help plz" requests into clear findings, evidence, and rectification actions.

Key Responsibilities

• QA external ML outputs (RegGenome): validate classifications, mappings, and relevance; detect anomalies and systemic errors; prioritise issues by user impact
• Correlation analysis at scale: explore patterns across jurisdictions, topics, entities, and time; quantify how model behaviours affect user experience (e.g., false positive load, missed material changes)
• Rectification workflow: document defects with reproducible evidence, maintain an issue taxonomy, feed into vendor and internal backlogs, and track resolution against SLAs
• Methodology development: design HITL review protocols, define sampling and acceptance criteria, and implement bulk/automated QA checks for our own ML outputs
• Cross-functional collaboration: partner with Product, Engineering to align QA priorities to business relevance and regulatory risk
• Reporting & auditability: produce clear QA reports, dashboards, and audit trails suitable for internal stakeholders and vendors
• Respond to data quality queries from internal stakeholders (e.g., Teams "help plz") and external parties (clients, vendors, auditors): triage, replicate, broaden the search to find similar cases, estimate scope, and communicate impact and next steps
• Drive issues through rectification to closure: categorise under an error taxonomy, assign owner (vendor vs internal), track via Jira, verify fixes against acceptance criteria, and report status updates
• Maintain an auditable trail: document hypotheses, queries/scripts, sampling results, and decisions; ensure responses are client/auditor‑ready
• Vendor interaction: regular QA reviews with Vendor using structured evidence packs and agreed SLAs.
• Query handling expectations: triage within 2 business hours, first findings within 24 hours, evidence pack within 48 hours for priority issues

Skills, Knowledge and Expertise

• Working knowledge of regulatory frameworks and the regulatory lifecycle; able to interpret regulatory documents sensibly
• Strong analytical mindset with hypothesis testing, sampling, and anomaly detection experience
• Practical data skills: database driven SQL for analysis; basic Python or equivalent scripting for bulk checks; experience with data visualisation
• Excellent communication for vendor discussions and cross-functional teamwork; rigorous documentation habits
• Able to translate ambiguous, ad‑hoc asks into testable hypotheses and measurable outputs; strong service mindset and urgency
• Clear written and verbal communication for internal and client/vendor audiences; comfortable handling sensitive data quality conversations
• Self discipline
• Ability to work with limited guidance; initiative taking
• High attention to detail; comfort with ambiguity and curiosity to dig until the signal is clear

Nice to have:

• 2–4 years in regulatory/compliance (financial services preferred)
• Background in law, regulation or regulatory affairs
• Experience QA'ing ML outputs, annotation/HITL processes, and defining QA taxonomies/acceptance criteria
• Familiarity with audit trail requirements and delivering defensible QA evidence
• Experience with Jira or similar for rectification workflows

Why join us?

• Shape the quality backbone of a high-impact regulatory intelligence platform used by leading financial institutions
• Work at the intersection of ML, regulation, and product—turning messy regulatory change into clear, actionable intelligence
• Real ownership: your QA methods and insights will directly improve user experience and client outcomes.

Benefits

• 25 days' paid holiday plus UK bank holidays
• Healthcare contribution
• Annual pay review
• Anything in your role you would like to expand on and build professional skills, you are welcome to let us know and we can help you, put you on courses etc.
• Monthly Socials (fun games with gift vouchers)
• Company Laptop