UK

United Kingdom · Operations · Director/VP (16-20 years)

Director of Quality Intelligence

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

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  • Experience bandDirector/VP (16-20 years)
  • Direct reports25-100+ reports
  • Reports toVP, Operations or Chief Operations Officer (COO)
  • UK framework levelUsually a director, accountable for a division and its numbers

Also advertised as VP, Quality Analytics · Head of Operations Excellence (Data) · Divisional Quality & Data Lead

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

ETCAL Level 4 NVQ Diploma in Business Improvement Techniques ETC Awards Limited · Level 4 · covers 2 of 10 standards
EAL Level 4 NVQ Diploma in Engineering Manufacture Excellence, Achievement & Learning Limited · Level 4 · covers 2 of 10 standards
EAL Level 4 NVQ Extended Diploma in Engineering Manufacture Excellence, Achievement & Learning Limited · Level 4 · covers 2 of 10 standards
OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 3 of 10 standards
NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 2 of 10 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 2 of 10 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Leading the application of Six Sigma metrics to a project ETCAL Level 4 NVQ Diploma in Business Improvement Techniques
Leading the application of basic statistical analysis EAL Level 4 NVQ Diploma in Engineering Manufacture
Data Analysis and Visualisation OTHM Level 7 Diploma in Data Science
Introduction to Data Science and Big Data NCC Education Level 5 Extended Diploma in Computing with Data Science
Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Automation Fluency building AI into how work runs
Trust Fluency keeping the digital world safe
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Your Plan Illustration

Built for Director of Quality Intelligence

5 units that map to this job, from the qualifications that cover it.

  1. Leading the application of Six Sigma metrics to a project ETC Awards Limited · covers 2 of 10 standards Level 4
  2. Leading the application of basic statistical analysis Excellence, Achievement & Learning Limited · covers 2 of 10 standards Level 4
  3. Data Analysis and Visualisation OTHM Qualifications · covers 3 of 10 standards Level 7
  4. Introduction to Data Science and Big Data NCC Education Limited · covers 2 of 10 standards Level 5
  5. Data Analytics Pearson Education Ltd · covers 2 of 10 standards Level 5
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Director of Quality Intelligence?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • AI Ethics & Governance for Operational Data
  • Industrial Internet of Things (IIoT) Data Architecture & Strategy
Why each one is rising

The future skills this job leans on

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your Director of Quality Intelligence plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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3What this role really is

A Director of Quality Intelligence shapes quality strategy for a major division or region, using data to guide business decisions.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

ERP / MES (e.g., SAP S/4HANA, Oracle NetSuite, Plex) Strategic

Influencing ERP module upgrades or MES selection based on data and analytics requirements; leading projects to integrate ERP quality data with other enterprise systems; ensuring data integrity from source systems.

Statistical Software (e.g., Minitab, JMP, Python w/ SciPy/statsmodels) Strategic

Determining the standard statistical toolset for the organisation; evaluating and championing new analytical methodologies; setting the strategic direction for advanced statistical modelling and predictive analytics.

BI & Visualization Platforms (e.g., Tableau, Power BI, Qlik Sense) Strategic

Governing the enterprise BI strategy for Operations quality; ensuring dashboards align with executive-level strategic objectives; driving adoption of self-service analytics across the division.

Database & Querying (e.g., SQL Server, PostgreSQL, Snowflake) Architect

Designing database schemas for quality data marts; optimising query performance for large datasets; working with DBAs and Data Engineers on ETL processes and data governance for enterprise quality data.

Advanced Analytics & Machine Learning (e.g., Python w/ pandas, scikit-learn, TensorFlow) Expert

Defining the strategy for developing and deploying machine learning models for predictive quality and prescriptive maintenance across the division; owning the end-to-end Python analytics stack for the quality function.

GRC / QMS Platforms (e.g., ServiceNow GRC, Veeva QualityDocs, MasterControl) Strategic

Utilising GRC platforms to manage quality policies and monitor compliance at an enterprise level; ensuring data systems provide auditable trails (data lineage) for regulatory bodies and external audits.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
Strategic Direction & Roadmap Follows defined processes, escalates deviations. Proposes minor process improvements within defined guidelines. Defines workstream-level strategy, recommends changes to overall roadmap.
Budget Allocation No budget authority, requests resources from supervisor. Manages small project budgets (<£5K) with manager approval. Approves project expenses up to £25K, consults on larger spend.
Team Structure & Hiring No direct reports, no hiring authority. No direct reports, may interview junior candidates. Mentors 0-2 junior analysts, provides input on hiring decisions.
Technology & Tool Selection Uses approved tools, suggests minor improvements. Selects appropriate tools for routine tasks from approved list. Recommends new tools for specific projects, evaluates vendors.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Cost of Poor Quality (COPQ) Reduction
The total financial cost of failures, including scrap, rework, warranty claims, and lost sales, across your division.
Target · Achieve a documented £5M+ annual reduction in COPQ.

Reduced divisional COPQ from £25M to £18M in a single fiscal year, directly attributable to quality intelligence initiatives.

Divisional Process Capability (Cpk/Ppk) Improvement
The average improvement in Cpk/Ppk across critical manufacturing processes within your remit.
Target · Increase average Cpk on key processes by 15% year-over-year.

After implementing new analytical models, 8 out of 10 critical production lines saw their Cpk improve from 1.1 to 1.3 within 12 months.

Predictive Quality Program Deployment & Efficacy
The successful rollout and measurable impact of advanced predictive quality analytics across multiple manufacturing sites.
Target · Deploy predictive quality models across 3+ manufacturing sites, reducing critical defect rates by 20%.

Rolled out a new machine learning model to three plants, leading to a 25% reduction in 'out-of-spec' batches and 10% less material waste.

Data Literacy & Analytical Maturity Score
The measurable improvement in the organisation's ability to understand, interpret, and act on quality data.
Target · Improve the divisional analytical maturity score from 'Basic' to 'Advanced' within a 2-year period (as per internal assessment framework).

Internal surveys and capability assessments showed a 30% increase in senior managers' confidence in using quality data for decision-making and a 20% increase in data-driven process changes.

Strategic Influence & Thought Leadership
Your ability to shape the broader Operations strategy by bringing data-driven insights to the executive table.
  • Regularly invited to present at executive leadership meetings
  • your recommendations are consistently adopted for major capital expenditure or process changes
  • you're seen as the go-to expert for data-driven quality discussions across the business unit.
Team & Talent Development
How well you build, mentor, and retain a high-performing team of quality data professionals.
  • High team retention rates
  • clear succession planning for key roles
  • successful promotion of team members
  • positive feedback in 360-degree reviews regarding your leadership and development support
  • your team is recognised internally and externally for their expertise.
Cross-Functional Integration & Collaboration
Your effectiveness in breaking down silos and integrating quality data and insights with other critical functions like Supply Chain, Engineering, and Finance.
  • Joint projects with measurable outcomes across departments
  • established data-sharing agreements and integrated reporting
  • positive feedback from peer directors on collaboration and alignment
  • quality metrics are consistently incorporated into broader business performance reviews.
Regulatory & Audit Preparedness
The robustness of our data systems and processes in supporting regulatory compliance and external audits.
  • Successful, no-major-findings external audits related to quality data
  • proactive identification and mitigation of compliance risks
  • your team consistently provides clear, auditable data trails when requested.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Driving Large-Scale Transformation

You'll spend time presenting to senior leadership, convincing them to invest in new quality tech, and working with plant managers to roll out new data collection processes. It's about fundamentally changing how we operate, not just tweaking things.

Leading the charge to implement a new predictive quality platform across all European manufacturing sites, seeing the direct impact on reduced defects and improved customer satisfaction.

Building High-Performing Teams & Capabilities

A big part of your day will involve mentoring your managers, identifying skill gaps in your team, and designing training programmes. You'll be thinking about how to build the 'next generation' of quality data analysts.

Developing a new internal certification program for Six Sigma Black Belts, significantly increasing the analytical horsepower within the Operations division.

Strategic Business Impact & Problem Solving

You'll be tackling complex, ambiguous problems that cut across multiple functions—think about how raw material quality impacts final product warranty claims and how to use data to prevent it. It's about solving problems that genuinely move the needle for the entire business unit.

Using advanced analytics to pinpoint a systemic quality issue originating from a specific supplier, leading to a revised procurement strategy and a 15% reduction in material-related defects.

What frustrates people
  • Strategic initiatives getting deprioritised due to 'urgent' operational firefighting.
  • Resistance from entrenched operational leaders to adopt new data-driven methodologies.
  • The sheer complexity of integrating disparate legacy data systems across multiple sites.
  • Securing the necessary budget and resources for ambitious quality intelligence programmes.
  • Translating highly technical analytical concepts into digestible, compelling narratives for non-technical executives.
What this role does not give you
  • Daily, hands-on data analysis or model building (you'll oversee, not execute).
  • A perfectly clean, well-structured data environment (you'll be driving the clean-up).
  • Immediate, short-term project cycles (strategic change takes time).
  • A role where all your recommendations are immediately adopted without negotiation.

8Who you work with

This role directly shapes the quality and operational excellence strategy for a significant business unit or region. You'll be accountable for improving process capability, reducing the Cost of Poor Quality (COPQ), and driving a data-first culture across multiple sites. Your decisions will influence multi-million-pound investments in technology and process improvements, ultimately impacting our market position and profitability.

Inside the business
  • VP, Operations (your direct line)
  • Regional Plant Directors
  • Head of Supply Chain
  • Head of Engineering & R&D
  • Finance Director
  • Global IT & Data Architecture Teams
  • Legal & Regulatory Compliance
Outside the business
  • Key Suppliers and Partners (for quality integration)
  • Industry Bodies and Standards Organisations
  • Regulatory Auditors
  • Strategic Technology Vendors

9What you need before you start

Not a wish list. The things you would be expected to already have.

  • Proven track record of leading and scaling data analytics functions within an Operations or manufacturing environment.
  • Demonstrated ability to influence C-suite and senior leadership with data-driven insights and strategic recommendations.
  • Extensive experience in managing large budgets (£2M+) and driving significant P&L impact through quality initiatives.
  • Deep expertise in Six Sigma, Lean, or other operational excellence methodologies, with a focus on data application.
  • A strong history of building, mentoring, and retaining high-performing analytical teams.
  • Experience in architecting and deploying enterprise-level data platforms and analytical solutions.

10What to practise next

Where the job is going, and what to do about it starting this week.

Advanced Data Governance & Quality Frameworks

As data volumes grow and regulations tighten, robust data governance isn't just a 'nice to have'; it's a strategic imperative. You'll need to define and implement enterprise-level frameworks to ensure data quality, lineage, and compliance across all operational data sources.

Data lineage and metadata management · Data stewardship models for operational data · Master Data Management (MDM) for quality attribute · Automated data quality monitoring and remediation · Compliance auditing for data quality standards

  • This quarter: Review our current data governance policies and identify gaps specific to quality data.
  • This month: Work with IT to map critical quality data elements and their sources.
  • Next quarter: Develop a proposal for a divisional data stewardship programme for quality data.
  • Month 6: Implement automated data quality checks for two critical production datasets.

Quick win: Identify the top three 'messiest' datasets your team struggles with and initiate a project to define clear ownership and data quality rules for them.

AI/ML Model Deployment & MLOps Strategy

Building models is one thing; deploying and managing them in a production operational environment is another. You'll need to understand the principles of MLOps to ensure your team's predictive quality models are scalable, reliable, and continuously monitored.

Model lifecycle management (development to deploym · Continuous Integration/Continuous Deployment (CI/C · Model monitoring and drift detection · Feature stores for consistent data access · Scalable inference for real-time predictions

  • This quarter: Familiarise yourself with MLOps best practices and platforms (e.g., Azure ML, AWS SageMaker).
  • This month: Work with your Lead Analysts and IT to define a standard deployment pipeline for new predictive quality models.
  • Next quarter: Pilot an MLOps platform for one critical predictive quality model.
  • Month 6: Establish clear SLAs for model performance and retraining in production.

Quick win: For any existing predictive models, define a clear process for how they are monitored for performance degradation and when they should be retrained. Don't let them 'set and forget'.

11Staying current once you are in

What people here do to keep up
  • Regularly attend and present at industry conferences (e.g., ASQ World Conference, Gartner Data & Analytics Summit) to stay abreast of trends and represent our organisation.
  • Engage in continuous learning around AI/ML ethics, governance, and advanced MLOps practices.
  • Participate in executive leadership development programmes focused on organisational change and strategic influence.
  • Publish thought leadership articles or white papers on data-driven quality in relevant industry journals.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

AI Ethics & Governance for Operational Data

As we deploy more AI and machine learning models in critical operational areas (like predictive quality or automated decision-making), understanding the ethical implications, bias, and governance frameworks becomes paramount. Regulators are starting to pay attention, and our reputation depends on it.

  • Algorithmic bias detection and mitigation in predi
  • Explainable AI (XAI) for operational transparency
  • Data privacy and security in AI systems
  • Regulatory frameworks for AI in manufacturing (e.g
  • Establishing internal AI governance policies for q

Industrial Internet of Things (IIoT) Data Architecture & Strategy

The sheer volume and velocity of data coming from IIoT sensors, smart machines, and connected devices are exploding. Your role will shift from analysing historical data to architecting real-time data streams and making sense of massive, unstructured datasets to drive predictive and prescriptive quality.

  • Edge computing for real-time data processing
  • Data lakes and data meshes for IIoT data storage
  • Streaming analytics platforms (e.g., Kafka, Flink)
  • Cybersecurity for IIoT devices and data pipelines
  • Integration of IIoT data with ERP/MES and QMS

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Statistical Process Control (SPC) & Advanced Statistics
  • Root Cause Analysis (RCA) & Problem Solving Methodologies
  • Measurement System Analysis (MSA) & Data Integrity
  • Design of Experiments (DOE) for Process Optimisation
  • Failure Mode and Effects Analysis (FMEA) & Risk Management

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    From Quality Analytics Manager (Internal)

    3-5 years as a Manager

    Skills to master

    • Mastering departmental P&L, developing a strong talent pipeline, driving cross-functional initiatives, and presenting to senior leadership beyond your direct line.

    You're ready to move on when

    • Successfully led a team to deliver a £1M+ annual COPQ reduction.
    • Consistently received positive feedback on talent development and team retention.
    • Demonstrated ability to influence and collaborate with peer directors.
    • Developed and presented strategic proposals to the VP of Operations.
  2. 2

    From Lead Quality Data Analyst (Internal, accelerated)

    5-7 years as a Lead Analyst, potentially with a strong MBA or executive programme

    Skills to master

    • Moving from technical leadership to people and strategic leadership, managing larger budgets, and developing executive presence. This path is less common but possible for exceptional talent.

    You're ready to move on when

    • Architected and deployed a major analytical solution across multiple sites.
    • Mentored multiple junior analysts to promotion.
    • Demonstrated strong project management of complex, multi-stakeholder programmes.
    • Actively sought out and successfully completed leadership development programmes.
  3. 3

    External Hire from a similar Director/VP role

    Direct entry

    Skills to master

    • Quickly understanding our specific operational context, data landscape, and organisational culture. Building immediate credibility with senior leadership and key stakeholders.

    You're ready to move on when

    • Proven track record in a comparable role within a similar industry.
    • Strong network and references from previous executive leadership.
    • Demonstrable ability to quickly assess and develop strategic plans for new environments.
    • Excellent cultural fit and alignment with our values.

15Where this role leads

The long view: This role can lead to positions such as Chief Operations Officer or Chief Data Officer, overseeing operations or data strategy across an organisation.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Director of Quality Intelligence is actually changing.

Where it leads

Roles this leads to

Also named as a next step, with no page in the catalogue yet:

  • VP, Operations (Broader Scope)

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Leading the application of Six Sigma metrics to a project, worked against Director of Quality Intelligence.

Leading the application of Six Sigma metrics to a project Level 4

Applied to your work in Director of Quality Intelligence

The objective of this unit is to enable learners to lead the application of Six Sigma metrics to projects, driving improvements through data-driven decision-making. Learners will develop plans for applying Six Sigma, delegate tasks for data collection and analysis, and track project progress against established metrics, making necessary adjustments to achieve desired outcomes.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

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See Your Progress Grow Illustration
Director of Quality Intelligence
  • Statistical Process Control (SPC) & Advanced Statistics
  • Root Cause Analysis (RCA) & Problem Solving Methodologies
  • Measurement System Analysis (MSA) & Data Integrity
  • Design of Experiments (DOE) for Process Optimisation
  • Failure Mode and Effects Analysis (FMEA) & Risk Management
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Director of Quality Intelligence is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. VP, Quality & Operational Excellence

    3-5 years in the Director role

    Move from divisional to enterprise-wide strategic accountability.

    • Global Quality Management System (QMS) ownership and governance.
    • Strategic oversight of all operational excellence programmes (Lean, Six Sigma, TPM) across the enterprise.
    • Defining and driving the company's overall operational data strategy.
    • Representing the company externally on all matters of quality and operational performance.

  2. VP, Operations (Broader Scope)

    4-6 years in the Director role

    Expanding beyond quality data to encompass broader operational leadership.

    • Advanced supply chain optimisation and network design.
    • Manufacturing strategy and capital investment planning.
    • Global logistics and distribution management.
    • Labour relations and workforce planning for large operational teams.
Working with AI on the job

Working with AI

Where AI is starting to help

As a Director, your time is gold. You're not just doing the work; you're shaping the strategy, leading your team, and influencing the entire division. Imagine if you could cut through the noise, accelerate strategic insights, and empower your team to be more effective, all while reclaiming valuable hours. That's where AI comes in.

AI isn't just for junior analysts anymore. For a Director of Quality Intelligence, it's a strategic amplifier. It helps you gain deeper insights faster, streamline complex processes, and communicate your vision more effectively, freeing you up to focus on the big picture and drive real transformation.

Automated Anomaly Detection (Divisional Oversight)

Instead of waiting for manual reports, use AI models to automatically monitor real-time sensor and production data across all your sites. Get immediate alerts on deviations that signal potential quality issues, allowing you to direct your teams to investigate proactively. This means fewer surprises and faster strategic interventions.

Predictive Quality Strategy & Deployment

Lead the charge in deploying machine learning models that predict product quality based on upstream process variables. This shifts your division from reactive quality control to proactive prevention, allowing you to strategise resource allocation and process adjustments before defects even occur. It's about building a future-proof quality system.

ISO & Regulatory Compliance Intelligence

Use AI assistants to rapidly research, summarise, and compare complex quality standards (e.g., ISO, IATF) and regulatory requirements. This helps you quickly assess compliance gaps across your division, prepare for audits with unprecedented speed, and design robust, compliant data collection plans for new initiatives. It's like having a legal and compliance expert on demand.

Executive Report & Board Presentation Drafting

After your team completes their analyses, use AI to generate first drafts of executive summaries, board presentation slides, and strategic proposals. Translate complex statistical findings into plain-language narratives that resonate with C-suite stakeholders, saving you hours of drafting and refining. Focus on the message, not the mechanics.

Common questions

Common questions

How do you become a Director of Quality Intelligence?

Common routes in include From Quality Analytics Manager (Internal) (3-5 years as a Manager), From Lead Quality Data Analyst (Internal, accelerated) (5-7 years as a Lead Analyst, potentially with a strong MBA or executive programme) and External Hire from a similar Director/VP role (Direct entry). Times vary with prior experience.

Where can a Director of Quality Intelligence progress to?

This role can lead on to VP, Quality & Operational Excellence (3-5 years in the Director role) and VP, Operations (Broader Scope) (4-6 years in the Director role), depending on the skills you build.

What new skills matter most for a Director of Quality Intelligence?

Increasingly, AI Ethics & Governance for Operational Data and Industrial Internet of Things (IIoT) Data Architecture & Strategy. These are the skills our record of this role lists as growing.

Free tools

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17Where to go from here

Other roles at Level 7

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in operations

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills built here, strategic data leadership, operational change, and working with senior leaders, apply beyond one industry. Similar senior roles exist in pharmaceuticals, automotive, aerospace, logistics, and technology companies with large physical operations.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my Director of Quality Intelligence plan, free What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.