Carbon Analyst

Role Purpose & Context

Role Summary

As a Carbon Analyst, you'll be responsible for gathering, inputting, and doing the first pass of validation on all the activity data we need to calculate our carbon footprint. This directly impacts our public reporting and how we make decisions about reducing emissions. You'll sit right at the start of our climate strategy process, taking raw information like electricity bills and fuel logs, and turning it into something our senior analysts can actually use. When you do this job well, our emissions data is accurate, auditable, and ready for public scrutiny. Get it wrong, and we risk making bad decisions or, worse, getting accused of greenwashing – which is a big deal for our reputation. The tricky part is that the data often comes from all over the place, in different formats, and sometimes it's just plain messy. But the reward? You'll be playing a really important part in our journey to net-zero, learning the fundamentals of climate accounting that underpin everything we do.

Reporting Structure

• Reports to: Climate Strategy Specialist (Level 2)
• Direct reports:
• Matrix relationships: Junior Sustainability Analyst, ESG Data Assistant, Climate Data Coordinator,

Key Stakeholders

Internal:

• Facilities Managers (for utility bills, fuel data)
• Procurement Team (for supplier data)
• Logistics & Operations Teams (for transport and operational data)
• Finance Department (for spend data and budget codes)
• Senior Climate Strategists (who'll use your data)

External:

• Third-party assurance providers (who'll audit your data)
• Sustainability reporting platforms (e.g., CDP, TCFD)

Organisational Impact

Scope: Your work directly underpins our entire carbon reporting and decarbonisation strategy. Accurate data means credible public disclosures and effective decision-making. If the data's off, everything else that follows will be too, which can really hurt our reputation and slow down our climate action.

Performance Metrics

Quantitative Metrics

1. Metric: Data Entry Accuracy
2. Desc: The percentage of data points entered that are free from errors (e.g., typos, incorrect units).
3. Target: <1% error rate
4. Freq: Monthly spot checks and during quarterly assurance reviews
5. Example: Out of 500 data points entered for Scope 1 emissions, you've only had 2 minor transcription errors, hitting a 99.6% accuracy rate. That's what we're after.
6. Metric: Data Collection Coverage
7. Desc: The proportion of required activity data (e.g., utility bills, fuel receipts) successfully gathered for a given reporting period.
8. Target: 95% primary data coverage for Scope 1 & 2
9. Freq: Quarterly, against a predefined list of required data sources
10. Example: For Q1, you managed to collect 98 out of 100 expected electricity bills from our sites across the UK, meaning we didn't have to estimate much.
11. Metric: Timeliness of Data Submission
12. Desc: Meeting internal deadlines for submitting validated activity data to the senior team for inventory calculation.
13. Target: All data submitted 2 days before the internal deadline
14. Freq: Per reporting cycle (quarterly/annually)
15. Example: You handed over the Q2 transport fuel data for all our fleet vehicles by Tuesday, giving the Climate Strategy Specialist plenty of time to run the calculations before Friday's deadline.

Qualitative Metrics

1. Metric: Adherence to Data Protocols
2. Desc: Following our established procedures for data collection, validation, and documentation, even when it feels a bit slow.
3. Evidence: Your data logs are always complete, showing who provided what, when, and how it was checked. You're using the right templates and naming conventions. Your manager doesn't have to remind you about the process.
4. Metric: Proactive Discrepancy Identification
5. Desc: Spotting unusual data points or potential errors and flagging them for review, rather than just inputting them blindly.
6. Evidence: You'll bring up things like 'This month's electricity bill for the London office is 30% higher than last month – should I check with Facilities?' or 'This fuel receipt looks like it's for a personal car, not a company vehicle.' You're thinking about the data, not just copying it.
7. Metric: Learning & Application of GHG Protocol
8. Desc: Demonstrating a growing understanding of GHG accounting principles and applying them correctly in your daily tasks.
9. Evidence: You can explain why we need both location-based and market-based Scope 2 data. You'll start asking intelligent questions about emission factors or data boundaries. Your mistakes become less frequent as you learn.

Primary Traits

• Trait: Analytical Rigor
• Manifestation: You're the person who triple-checks if that emission factor is from the right year or if the units on the utility bill actually match what you're inputting. You build spreadsheets that are easy for someone else to audit, because you know the numbers have to stand up to scrutiny. You'll spot the one incorrect utility bill in a dataset of thousands, almost instinctively.
• Benefit: Our climate data gets published in public reports and scrutinised by investors, regulators, and assurance providers – think PwC or Deloitte. A single calculation error isn't just a minor slip; it can lead to accusations of greenwashing and seriously damage our corporate reputation. We need people who are naturally meticulous with numbers.
• Trait: Eagerness to Learn & Ask Questions
• Manifestation: You're not afraid to put your hand up and ask 'Why?' or 'How does this work?' when something doesn't quite make sense. You'll read up on the GHG Protocol in your own time, or ask your manager to explain the difference between Scope 1 and Scope 3. You see every new piece of data or process as a chance to understand more, not just another task to tick off.
• Benefit: This field changes constantly, and the data is complex. If you're not actively trying to understand the 'why' behind what you're doing, you'll struggle to spot errors or adapt to new requirements. We need people who are genuinely curious and want to master the craft of carbon accounting, not just follow instructions.
• Trait: Organised & Methodical
• Manifestation: Your files are always named consistently, your data is saved in the right place, and you know exactly where to find that obscure supplier invoice from last quarter. You've got a system for tracking your data requests and follow-ups. You don't just 'do' the work; you do it in a way that makes it easy for anyone else to pick up where you left off.
• Benefit: We're dealing with hundreds, sometimes thousands, of data points from dozens of sources. If your work isn't organised, it quickly becomes a chaotic mess, making it impossible to audit, report, or even find what we need. Being methodical means we can trust the process and the output.

Supporting Traits

• Trait: Resilience
• Desc: You'll often be chasing data from people who are busy and don't always see it as their top priority. You'll need to be able to politely follow up, sometimes multiple times, without getting discouraged.
• Trait: Patience
• Desc: Gathering all the necessary data takes time. It's rarely a quick process, and you'll need to be patient with both the process and the people you're working with.
• Trait: Intellectual Curiosity
• Desc: The world of climate science, policy, and technology is always evolving. A natural curiosity about these areas will help you understand the context of your work and grow in the role.

Primary Motivators

1. Motivator: Making a Tangible Impact on Climate Action
2. Daily: You're motivated by the idea that your meticulous data entry and validation directly contribute to our company's ability to set credible climate targets and reduce real-world emissions. You'll feel a sense of purpose knowing your work isn't just busywork.
3. Motivator: Mastering a Complex, In-Demand Skillset
4. Daily: You're excited by the prospect of becoming an expert in GHG accounting and climate data management. You'll enjoy the challenge of learning new methodologies and understanding the nuances of different emission scopes.
5. Motivator: Working in a Purpose-Driven Team
6. Daily: You'll thrive in an environment where everyone is genuinely committed to sustainability. You'll enjoy collaborating with colleagues who share your values and are all working towards a common, important goal.

Potential Demotivators

Honestly, this role involves a fair bit of repetitive data entry and chasing people for information. If you're someone who needs constant novelty or gets frustrated by administrative tasks, you might find parts of this challenging. You'll sometimes feel like a data scavenger, spending more time tracking down information than actually analysing it.

Common Frustrations

1. Spending 60% of your time chasing down operational data (utility bills, fuel logs, refrigerant leakage reports) from dozens of different facility managers who often see you as an administrative burden.
2. Dealing with data that arrives in inconsistent formats – some in spreadsheets, some in PDFs, some just scribbled on a piece of paper.
3. Having to explain *again* why we need specific data points, even after you've sent clear instructions.
4. Seeing an 'urgent' data request get deprioritised by another department because they've got their own fires to put out.

What Role Doesn't Offer

1. High-level strategic decision-making (not yet, anyway – you'll be supporting it).
2. A lot of client-facing work (it's mostly internal data collection).
3. A 'set it and forget it' routine (the data sources and requirements can shift).
4. Immediate, visible impact on every single piece of work you do (some of your data will feed into much larger reports).

ADHD Positives

1. The 'data scavenger hunt' aspect can be really engaging for those with ADHD, as it involves varied tasks, problem-solving, and a sense of discovery when you find that elusive piece of information.
2. The need for meticulous attention to detail can be a strength, especially when hyperfocus allows for deep dives into data sets, catching errors others might miss.
3. The clear, structured protocols for data entry and validation can provide a helpful framework, reducing ambiguity.

ADHD Challenges and Accommodations

1. Repetitive data entry can be a challenge; we can explore tools for automation or break up tasks to maintain engagement.
2. Chasing stakeholders requires sustained focus; we can use structured follow-up systems and provide templates to make it less daunting.
3. We offer flexible working arrangements (within core hours) and a quiet workspace option to help manage distractions.

Dyslexia Positives

1. The visual nature of data analysis, once the data is in a structured format (e.g., spreadsheets, dashboards), can be very intuitive and a strong suit.
2. Strong spatial reasoning skills, often associated with dyslexia, can be valuable in understanding data relationships and identifying patterns or anomalies.
3. The focus on numerical accuracy over extensive written reports can play to strengths.

Dyslexia Challenges and Accommodations

1. Reading through large volumes of unstructured text (e.g., reports, emails from stakeholders) for data points might be challenging; we encourage the use of text-to-speech software and provide clear, concise instructions.
2. Ensuring accuracy in written documentation can be difficult; we use templates, provide grammar and spell-checking tools, and offer peer review for important documents.
3. We're happy to discuss specific software or tools that can make your reading and writing tasks easier.

Autism Positives

1. The methodical, logical nature of GHG accounting and data validation can be a great fit, as it often involves following clear rules and identifying discrepancies.
2. A strong preference for facts and data-driven approaches aligns well with the need for objective, auditable emissions reporting.
3. The ability to focus deeply on specific tasks, like cleaning a complex dataset, can be a significant advantage.

Autism Challenges and Accommodations

1. Social interactions, particularly chasing data from multiple stakeholders, might be draining; we can provide clear scripts for communication and allow for email-based follow-ups where possible.
2. Unexpected changes in data requirements or processes can be unsettling; we aim to communicate changes well in advance and explain the 'why' behind them.
3. We offer a calm, predictable work environment with options for noise-cancelling headphones and dedicated focus areas.

Key Responsibilities

Experience Levels Responsibilities

1. Level: Entry Level (0-2 years)
2. Responsibilities: Gather activity data: You'll collect all sorts of raw data – think electricity bills, gas consumption, fuel purchase logs, and travel records – from various internal departments and sometimes external suppliers. (Yes, it's a bit of a treasure hunt, but an important one!)
3. Input data accurately: You'll enter this raw data into our carbon accounting platform (like Persefoni or Watershed) or our master spreadsheets, making sure every number is correct and in the right place.
4. Perform initial data validation: You'll do the first check on the data, looking for obvious errors, missing information, or strange spikes that don't make sense. If something looks off, you'll flag it to your manager.
5. Maintain data documentation: You'll keep clear records of where the data came from, who provided it, and any assumptions made. (Future-you, and our auditors, will thank you for this!)
6. Generate basic reports: You'll pull pre-built reports from our accounting software or create simple charts in Excel to help the senior team visualise the data.
7. Support ad-hoc data requests: Sometimes, other teams will need a quick bit of emissions data for a project or presentation. You'll help them out by finding and formatting what they need.
8. Learn GHG Protocol standards: You'll spend time understanding the basics of the Greenhouse Gas Protocol – what Scope 1, 2, and 3 emissions are, and why they matter. This is fundamental to everything we do.
9. Supervision: You'll have daily check-ins with your direct manager, especially when you're starting out. For more complex tasks or anything outside the usual routine, you'll be working closely with a senior team member. We won't throw you in the deep end without a life raft.
10. Decision: Honestly, you won't be making independent decisions on methodology or strategy. All your work, especially data validation and any communication with external parties, will be reviewed by your manager or a senior analyst before it goes out. If you're unsure about anything, you'll escalate it.
11. Success: You're doing well if your data entries are consistently accurate, you meet your deadlines for data collection, and you're actively asking questions to deepen your understanding of carbon accounting. Basically, we want to see you learning and delivering reliable data.

Decision-Making Authority

• Type: Data Source Selection (e.g., which utility provider's data to use)
• Entry: Propose to manager, manager decides.
• Mid: Decide based on established protocols, inform manager of any deviations.
• Senior: Define protocols, decide on complex cases, inform Director.
• Type: Data Validation & Error Flagging
• Entry: Identify potential errors, escalate to manager for review and decision.
• Mid: Investigate and resolve routine errors independently, escalate complex or systemic issues.
• Senior: Design validation processes, resolve systemic issues, approve exceptions.
• Type: Methodology for minor data gaps (e.g., estimating a missing month's data)
• Entry: Escalate to manager; manager provides guidance or makes the decision.
• Mid: Propose an estimation method based on guidelines, get manager's approval.
• Senior: Define and approve estimation methodologies, document rationale.
• Type: Communication with external data providers (e.g., asking for missing bills)
• Entry: Draft communication, manager reviews and approves before sending.
• Mid: Communicate independently following established templates, escalate non-responses.
• Senior: Establish communication protocols, manage difficult relationships.

Competency Requirements

Foundation Skills (Transferable)

These are the core 'human' skills you'll need to do well. They're not about specific software, but about how you think, communicate, and get things done.

• Category: Communication & Collaboration
• Skills: Active Listening: Really hearing what people are telling you about their data, not just waiting for your turn to speak. It means asking clarifying questions to make sure you understand the context of the numbers.
• Clear Written Communication: Writing concise emails for data requests that people actually understand and respond to. No jargon, just clear instructions.
• Teamwork: Working effectively with your manager and other analysts, sharing information, and helping out when needed. We're all in this together.
• Category: Problem-Solving & Critical Thinking
• Skills: Basic Data Problem-Solving: When a number looks wrong, you'll try to figure out why. Is it a typo? A missing unit? You'll do some detective work before escalating.
• Attention to Detail: Spotting that single misplaced decimal point in a huge spreadsheet. This is absolutely critical for carbon accounting, where small errors have big consequences.
• Organisational Skills: Keeping track of multiple data requests, deadlines, and different file versions. Your desk (and your digital folders) should be tidy.
• Category: Adaptability & Initiative
• Skills: Learning Agility: Being able to pick up new software, understand new reporting standards, and adapt to changes in data collection processes fairly quickly. The climate world moves fast.
• Proactive Follow-up: Not waiting to be told to chase someone for data. If a deadline is looming, you'll politely remind them. It's about taking ownership of your tasks.
• Time Management: Juggling multiple data requests and making sure you're prioritising what's most urgent. Sometimes, you'll have to ask your manager what's most important.

Functional Skills (Role-Specific Technical)

These are the specific knowledge areas and tools you'll be using day-to-day to get the job done. Don't worry if you're not an expert in everything – we're looking for a solid foundation and a willingness to learn.

Technical Competencies

• Skill: GHG Protocol Corporate Standard (Basic)
• Desc: Understanding the fundamental concepts of greenhouse gas accounting, including the difference between Scope 1, Scope 2, and Scope 3 emissions. You don't need to be an expert, but you should know the basics.
• Level: Basic
• Skill: Data Collection & Validation Principles
• Desc: Knowing how to gather activity data from various sources, and the initial steps to check if that data is complete, consistent, and accurate. It's about making sure the numbers make sense.
• Level: Intermediate
• Skill: Basic Decarbonisation Concepts
• Desc: An awareness of common ways companies reduce emissions, like switching to renewable energy or improving energy efficiency. You should understand *why* we're collecting this data.
• Level: Basic
• Skill: Sustainability Reporting Frameworks (Awareness)
• Desc: A general understanding that companies report on their climate performance using frameworks like TCFD or CDP, and that your data feeds into these. You don't need to know the details, just the context.
• Level: Basic

Digital Tools

• Tool: Microsoft Excel (Power Query, Pivot Tables)
• Level: Intermediate
• Usage: Cleaning and formatting raw data, building simple charts, using basic formulas (SUM, AVERAGE, VLOOKUP), and creating pivot tables to summarise data. You'll use it constantly.
• Tool: Persefoni / Watershed / Sphera (Carbon Accounting Software)
• Level: Intermediate
• Usage: Entering activity data into the platform, pulling pre-built reports, and validating data inputs against source documents. You'll be a regular user of one of these systems.
• Tool: Microsoft Power BI / Tableau
• Level: Basic
• Usage: Viewing and interacting with existing dashboards to extract information, but not necessarily building them from scratch. You'll be a consumer of these tools.
• Tool: MS Teams / SharePoint / Asana
• Level: Intermediate
• Usage: Collaborating with your team, managing your tasks, sharing documents, and participating in team discussions. These are our everyday communication tools.

Industry Knowledge

• Area: Environmental Data Management
• Desc: Understanding the principles of collecting, storing, and managing environmental data, including data quality control and version control. It's about keeping things tidy and traceable.
• Area: Basic Climate Science
• Desc: A foundational understanding of climate change, greenhouse gases, and why reducing emissions is important. This provides the 'why' behind your daily tasks.

Regulatory Compliance Regulations

• Reg: UK Streamlined Energy and Carbon Reporting (SECR)
• Usage: Understanding that your data contributes to our mandatory UK carbon reporting requirements, even if you're not directly preparing the report.
• Reg: Task Force on Climate-related Financial Disclosures (TCFD)
• Usage: Knowing that your data helps us disclose climate risks and opportunities to investors and other stakeholders, structured around TCFD's four pillars.

Essential Prerequisites

• A genuine interest in sustainability and climate action – you won't last long if you don't care about the mission.
• Strong numerical aptitude and comfort working with large datasets.
• Proficiency in Microsoft Excel (Intermediate level, as described above) or equivalent spreadsheet software.
• Excellent organisational skills and a methodical approach to tasks.
• The ability to communicate clearly and concisely, both in writing and verbally.
• A proactive attitude and a willingness to ask questions and learn.

Career Pathway Context

These are the absolute basics we need you to walk in with. We'll teach you the specific carbon accounting methodologies and software, but these foundational skills are what will allow you to pick it all up quickly and thrive in the role. Think of them as the building blocks for your future career in sustainability.

Qualifications & Credentials

Emerging Foundation Skills

• Skill: Prompt Engineering for Data Tasks
• Why: AI tools like ChatGPT and Claude are becoming incredibly powerful for tasks like summarising documents, cleaning messy text data, or even generating basic code snippets. Analysts who can 'talk' to these AIs effectively will be far more productive.
• Concepts: [{'concept_name': 'Clear Instruction Giving', 'description': 'Learning to write precise, unambiguous prompts to get the exact output you need from an AI.'}, {'concept_name': 'Context Windows', 'description': "Understanding how much information an AI can 'remember' at once and how to feed it relevant context."}, {'concept_name': 'Output Validation', 'description': "Knowing that AI isn't perfect and how to quickly check its work for accuracy and 'hallucinations'."}, {'concept_name': 'Iterative Prompting', 'description': 'Refining your prompts based on initial AI responses to get closer to your desired outcome.'}]
• Prepare: This week: Sign up for a free account with ChatGPT or Claude and try using it to summarise long emails or articles you receive.
• This month: Experiment with using AI to help you clean small, messy datasets in Excel – ask it to identify patterns or suggest corrections.
• Month 2: Try using AI to draft simple data request emails, then review and refine them yourself.
• Month 3: Explore how AI can help you generate ideas for basic data visualisations based on a dataset.
• QuickWin: Start using AI to draft email responses or summarise meeting notes right away. It's low-risk and gives you immediate practice with prompt writing.
• Skill: Basic Data Visualisation Storytelling
• Why: It's not enough to just collect data; you need to be able to present it clearly so people can understand what it means. Even at an entry level, being able to create a simple, impactful chart is a huge advantage.
• Concepts: [{'concept_name': 'Choosing the Right Chart Type', 'description': 'Knowing when to use a bar chart vs. a line chart vs. a pie chart for different types of data.'}, {'concept_name': 'Clarity & Simplicity', 'description': 'Making sure your charts are easy to read, with clear titles, labels, and minimal clutter.'}, {'concept_name': 'Highlighting Key Insights', 'description': 'Using colour or annotations to draw attention to the most important points in your data.'}, {'concept_name': 'Audience Awareness', 'description': "Tailoring your visualisations to who you're presenting to – a manager needs different detail than a factory floor worker."}]
• Prepare: This week: Watch a few YouTube tutorials on 'best practices for data visualisation in Excel' or 'how to make charts clear'.
• This month: For every piece of data you summarise, try to create at least one simple chart that tells a story.
• Month 2: Ask your manager or a senior analyst for feedback on your charts – what's clear, what's confusing?
• Month 3: Experiment with different chart types in Power BI or Tableau (even if just viewing existing dashboards) to see how they convey information.
• QuickWin: Whenever you present numbers, even informally, try to back them up with a simple, well-labelled chart. It's a small change with a big impact on clarity.

Advancing Technical Skills

• Skill: Advanced Carbon Accounting Software Usage
• Why: You'll move beyond just data entry. As you progress, you'll need to understand how the software calculates emissions, how to troubleshoot issues, and how to pull more complex, customised reports.
• Concepts: [{'concept_name': 'Emission Factor Management', 'description': 'Understanding how emission factors are applied and how to update them within the system.'}, {'concept_name': 'Data Integration Basics', 'description': 'Awareness of how different data sources connect to the platform (e.g., API connections, manual uploads).'}, {'concept_name': 'Custom Report Building', 'description': "Learning to build reports that aren't pre-set, to answer specific business questions."}]
• Prepare: This quarter: Ask your manager for a walkthrough of the platform's calculation logic for Scope 1 emissions.
• Next quarter: Take any internal training modules available for the carbon accounting software.
• Month 6: Try to troubleshoot a minor data discrepancy within the platform yourself before escalating.
• Month 9: Propose a new custom report that could provide valuable insights to the team.
• QuickWin: Spend 30 minutes each week just exploring the carbon accounting platform's less-used features. You might stumble upon something useful.

Education Requirements

• Level: Minimum
• Req: A-Levels (or equivalent) with a strong numerical component (e.g., Maths, Science, Economics)
• Alts: We're open to candidates with relevant vocational qualifications (e.g., BTEC in Environmental Science) or demonstrable experience in a data-heavy administrative role. Show us you can handle numbers and we're interested.
• Level: Preferred
• Req: A Bachelor's degree in Environmental Science, Sustainability, Data Science, Finance, or a related quantitative field.
• Alts: If you've got a degree, great, but it's not the be-all and end-all. Strong practical experience and a proven track record with data can absolutely substitute for a specific degree.

Experience Requirements

You'll need roughly 0-2 years of experience. This could be anything from an internship in a sustainability team, a data entry role in a different sector, or even a university project that involved a lot of data collection and analysis. What really matters is your comfort with numbers, your attention to detail, and a genuine interest in climate work. We're not expecting you to be an expert, but you should have some practical experience handling data.

Preferred Certifications

• Cert: GHG Protocol e-learning course completion
• Prod: World Resources Institute (WRI)
• Usage: Shows you've got a foundational understanding of the core methodology we use for carbon accounting. It's a great way to show initiative.
• Cert: Certified Associate in Project Management (CAPM)
• Prod: Project Management Institute (PMI)
• Usage: While not directly carbon-related, it demonstrates good organisational and project coordination skills, which are super helpful for managing data collection.

Recommended Activities

• Attend internal training sessions on our carbon accounting software and reporting processes.
• Complete online courses on Excel advanced functions (e.g., Power Query, advanced formulas).
• Read up on the latest climate news and policy developments (e.g., through industry newsletters, reputable climate news sites).
• Participate in sustainability webinars or conferences (even virtual ones) to broaden your understanding of the sector.
• Shadow a senior analyst to understand their workflow and how your data feeds into their work.

Career Progression Pathways

Entry Paths to This Role

• Path: Graduate Sustainability Programme
• Time: 0-1 year post-graduation
• Path: Data Entry / Administrative Assistant (with a sustainability focus)
• Time: 1-2 years in a previous role
• Path: Environmental Internship / Apprenticeship
• Time: 6-12 months internship/apprenticeship

Career Progression From This Role

• Pathway: Climate Strategy Specialist (Level 2)
• Time: 2-3 years in the Carbon Analyst role

Long Term Vision Potential Roles

• Title: Senior Climate Strategist (Level 3)
• Time: 5-8 years from entry
• Title: Lead Strategist, Climate & Decarbonisation (Level 4)
• Time: 8-12 years from entry
• Title: Manager, Climate Strategy & Reporting (Level 5)
• Time: 12-16 years from entry

How Zavmo Delivers This Role's Development

DISCOVER Phase: Skills Gap Analysis

Zavmo maps your current competencies against all requirements in this job description through conversational assessment. We evaluate your foundation skills (communication, strategic thinking), functional skills (CRM expertise, negotiation), and readiness for career progression.

Output: Personalised skills gap heat map showing strengths and priorities, estimated time to competency, neurodiversity accommodations.

DISCUSS Phase: Personalised Learning Pathway

Based on your DISCOVER results, Zavmo creates a personalised learning plan prioritised by impact: foundation skills first, then functional skills. We adapt to your learning style, pace, and neurodiversity needs (ADHD, dyslexia, autism).

Output: Week-by-week schedule, each module linked to specific job responsibilities, checkpoints and milestones.

DELIVER Phase: Conversational Learning

Learn through conversation, not boring modules. Zavmo uses 10 conversation types (Socratic dialogue, role-play, coaching, case studies) to build competence. Practice difficult QBR presentations, negotiate tough renewals, and handle churn conversations in a safe AI environment before facing real clients.

Example: "For 'Stakeholder Mapping', Zavmo will guide you through analysing a complex enterprise account, identifying key decision-makers, and building an engagement strategy."

DEMONSTRATE Phase: Competency Assessment

Zavmo automatically builds your evidence portfolio as you learn. Every conversation, practice scenario, and application example is captured and mapped to NOS performance criteria. When ready, your portfolio supports OFQUAL qualification claims and demonstrates competence to employers.

Output: Competency matrix, evidence portfolio (downloadable), qualification readiness, career progression score.