Analytics Consultant

Role Purpose & Context

Role Summary

The Analytics Consultant is here to independently manage and deliver key parts of our larger analytics projects. You'll be the one taking raw data, making sense of it, and then explaining your findings in a way that helps our internal clients—like Sales, Marketing, or Operations—actually improve how they work. You'll sit right at the intersection of data science and business strategy, translating technical analysis into practical advice. When you do this well, our business units get clear, actionable insights that genuinely move the needle on their objectives, whether that's saving money or increasing revenue. If you don't, well, decisions might be made on gut feeling, which usually costs us more in the long run. The tricky part is often dealing with messy data and sometimes, getting people to trust the numbers over their long-held beliefs. But the reward? Seeing your analysis directly lead to real, tangible improvements across the company.

Reporting Structure

• Reports to: Senior Analytics Consultant
• Direct reports: 0
• Matrix relationships: Junior Data Consultant, Business Intelligence Analyst, Insight Analyst, Data Analyst (Internal Consulting),

Key Stakeholders

Internal:

• Project Managers (Internal Consulting)
• Business Unit Leads (e.g., Head of Sales, Marketing Manager)
• Data Engineering Team
• Finance Business Partners
• Operations Managers

External:

• None (this is an internal consulting role)

Organisational Impact

Scope: This role directly helps our internal clients make better, data-driven decisions. Your analyses will shape tactical adjustments in various departments, improving efficiency, optimising processes, and identifying new opportunities. Get it right, and we save money or make more; get it wrong, and we might chase the wrong problems or miss out on growth.

Performance Metrics

Quantitative Metrics

1. Metric: Analysis Accuracy
2. Desc: The percentage of your analyses that are free from data errors or logical flaws after review.
3. Target: >98% accuracy
4. Freq: Per project, reviewed by Senior Consultant
5. Example: Your Q2 sales trend analysis had no errors identified during the Senior Consultant's review, hitting 100% accuracy for that piece of work.
6. Metric: On-Time Delivery Rate
7. Desc: The proportion of assigned tasks and project components delivered by the agreed-upon deadline.
8. Target: 95% of tasks delivered on time
9. Freq: Weekly/Bi-weekly, tracked in Jira
10. Example: Out of 20 tasks assigned last month, you completed 19 by their due date, resulting in a 95% on-time delivery rate.
11. Metric: Client Adoption Rate (of recommendations)
12. Desc: The percentage of your project recommendations that are actually implemented by the business unit you're advising.
13. Target: Roughly 60% adoption rate
14. Freq: Quarterly, post-project review
15. Example: You recommended three process changes to the Operations team; two were implemented within the quarter, giving you a 66% adoption rate for that project.
16. Metric: Query Efficiency
17. Desc: The average run-time of your SQL queries and Python scripts, aiming for optimisation.
18. Target: Reduce average query run-time by 15% over 6 months
19. Freq: Monthly, via code review and query logs
20. Example: Your monthly report script used to take 30 minutes; after optimising the joins, it now runs in 22 minutes, a 26% improvement.

Qualitative Metrics

1. Metric: Clarity of Communication
2. Desc: How well you explain complex analytical findings to non-technical audiences, making them easy to understand and act upon.
3. Evidence: Internal clients consistently tell us your presentations are clear. They'll ask fewer clarifying questions about the 'what' and more about the 'how'. Your written summaries get straight to the 'so what' without jargon. People actually read your reports.
4. Metric: Proactive Problem Identification
5. Desc: Your ability to spot potential business issues or opportunities in the data before being asked to look for them.
6. Evidence: You'll bring ideas to your Senior Consultant or project lead that weren't on the original brief. You might flag a worrying trend in customer behaviour or suggest an untapped market segment based on your regular data reviews. It's about curiosity and initiative.
7. Metric: Collaboration & Teamwork
8. Desc: How effectively you work with your immediate team and other departments to achieve project goals.
9. Evidence: Your colleagues will say you're easy to work with. You'll share your knowledge, offer help when others are stuck, and openly ask for feedback on your own work. You're a good listener in meetings, not just waiting to speak.
10. Metric: Adaptability to Changing Requirements
11. Desc: Your ability to adjust your approach and deliverables when project scopes or data sources inevitably shift.
12. Evidence: When a stakeholder changes their mind mid-project (and they will!), you don't just complain; you quickly re-scope the work and adjust your plan. You're comfortable with a bit of ambiguity and can pivot without losing momentum.

Primary Traits

• Trait: Intellectually Skeptical
• Manifestation: You never just take a number at face value. You'll always ask, 'Where did this come from?', 'What's the denominator here?', or 'What assumptions are we baking into this metric?' You'll naturally try to cross-reference data from a couple of different places to make sure it all adds up. It's about having a healthy dose of suspicion.
• Benefit: Honestly, you're a key guardian against bad data leading to poor decisions. A misplaced decimal or a misunderstood definition can quickly snowball into a significant financial mistake. We need someone who instinctively spots the oddity in a report or questions a forecast that looks a bit too good to be true, before it gets too far up the chain.
• Trait: Articulate & Concise
• Manifestation: You can take a complicated analysis and explain the key business takeaway to someone non-technical in about a minute. Your presentations get straight to the point, with slide titles that tell the story. You know when a simple bar chart is better than a fancy scatter plot, especially for busy managers. No jargon, just clarity.
• Benefit: Our internal clients are time-poor. If your complex analysis can't be quickly understood and acted upon, its value drops to zero. Your ability to cut through the noise and deliver a clear, actionable message is what turns data into real impact. No one wants to wade through a 50-page technical report.
• Trait: Pragmatic Problem Solver
• Manifestation: You understand that sometimes 'good enough' analysis delivered on time is much better than 'perfect' analysis delivered too late. You'll look for the quickest, most robust way to answer a business question, rather than getting bogged down in academic perfection. You're happy to make a reasonable assumption if it gets us to a useful answer faster, and you'll always state your assumptions clearly.
• Benefit: In internal consulting, speed often matters. We're here to support business decisions that often have tight deadlines. If you spend weeks polishing a model when a solid answer in days would suffice, we miss the boat. You need to balance analytical rigour with the realities of business velocity.

Supporting Traits

• Trait: Structurally Minded
• Desc: You enjoy breaking down big, vague problems into smaller, manageable chunks. It's like seeing a messy room and knowing exactly how to organise it.
• Trait: Curious
• Desc: You're genuinely interested in how the business works and why things happen. You don't just answer the question; you try to understand the question behind the question.
• Trait: Resilient
• Desc: You can bounce back quickly when a recommendation isn't taken on board, or when a project gets re-prioritised. It happens, and you don't take it personally.
• Trait: Patient Teacher
• Desc: You're willing to explain basic data concepts to non-technical colleagues without making them feel silly. You recognise that not everyone lives and breathes data like you do.

Primary Motivators

1. Motivator: Solving Tangible Business Problems
2. Daily: You'll get a real kick out of taking a messy, ill-defined business challenge and using data to figure out a clear path forward. It's the 'aha!' moment when your analysis directly leads to a practical solution for a colleague.
3. Motivator: Continuous Learning & Skill Development
4. Daily: You're always keen to pick up a new analytical technique, a different way to visualise data, or a better approach to structuring a problem. The idea of constantly refining your craft and adding new tools to your belt genuinely excites you.
5. Motivator: Making an Impact Without Direct Management
6. Daily: You enjoy influencing decisions through the power of your insights and recommendations, rather than through formal authority. You like being the expert who provides the evidence, letting others make the final call but knowing you've shaped their thinking.

Potential Demotivators

Honestly, this role isn't for everyone. You'll spend a fair bit of time wrestling with data that's not quite right, or trying to get two different departments to agree on what a 'customer' actually means. The 'urgent' request that blew up your Thursday might get quietly forgotten by Friday. And sometimes, after all your hard work, a recommendation might get ignored because someone higher up just 'has a feeling'. If you need every piece of your work to be perfectly clean, perfectly implemented, and always adopted, you'll probably find this frustrating.

Common Frustrations

1. Spending 60% of your time on 'data janitor duty'—cleaning, joining, and wrangling data from disparate, poorly documented systems before you can even start analysing.
2. When a stakeholder agrees to a project scope, but after seeing your initial results, they completely change the core question, forcing you to essentially restart.
3. Presenting a statistically sound, data-driven recommendation, only to have it vetoed by an executive based on a gut feeling or a single anecdote.
4. Your carefully planned week constantly being derailed by last-minute, 'urgent' data pull requests for a meeting that afternoon.
5. Wasting days navigating political turf wars just to get access to a critical dataset owned by a protective department head.
6. Being treated as a report factory, rather than a strategic partner whose insights are sought *before* key decisions are made.

What Role Doesn't Offer

1. Direct people management responsibilities (that comes later).
2. A perfectly clean, harmonised 'single source of truth' for all data (we're working on it, but it's a journey).
3. A guarantee that every single one of your brilliant recommendations will be implemented (politics is real).
4. A predictable, 9-to-5, no-surprises routine (expect some fire drills).

ADHD Positives

1. The varied nature of internal consulting projects means you're rarely stuck on one thing for too long, which can be great for those who thrive on novelty and diverse challenges.
2. The 'urgent fire drill' requests, while frustrating for some, can provide intense, short-term focus points that some ADHD profiles excel at, delivering under pressure.
3. The problem-solving aspect, especially breaking down complex, ambiguous problems, can be highly engaging and stimulating, tapping into hyperfocus.

ADHD Challenges and Accommodations

1. Maintaining focus on longer-term projects with less immediate gratification can be a challenge. We can help by breaking down tasks into smaller, more frequent deliverables and providing regular check-ins.
2. Documentation can feel tedious and might be overlooked. We use templates and have a culture of peer review to help ensure this essential task gets done.
3. Managing multiple competing 'urgent' requests can lead to overwhelm. We'll work with you on prioritisation frameworks and help you push back when necessary, ensuring you're not constantly context-switching.

Dyslexia Positives

1. The role relies heavily on visual data interpretation and pattern recognition, which are often strengths for dyslexic individuals.
2. Strong verbal communication is key for presenting insights, allowing for direct articulation of findings rather than solely relying on written reports.
3. The ability to see the 'big picture' and make connections across disparate data points can be a significant advantage in identifying strategic opportunities.

Dyslexia Challenges and Accommodations

1. Extensive written documentation and report writing can be time-consuming. We encourage the use of dictation software, grammar checkers (like Grammarly), and provide templates to streamline the process.
2. Proofreading your own work, especially complex analytical reports, can be difficult. We have a culture of peer review and encourage using text-to-speech tools for self-correction.
3. Reading large volumes of dense text (e.g., internal policy documents) might be tiring. We can offer tools like ClaroRead or provide summaries where appropriate.

Autism Positives

1. The logical, data-driven nature of analysis can be a great fit, allowing you to focus on facts and patterns rather than subjective interpretations.
2. The opportunity to specialise in specific data domains or analytical techniques can align well with deep-dive interests.
3. Clear expectations around quantitative metrics and structured problem-solving frameworks provide a predictable approach to work tasks.

Autism Challenges and Accommodations

1. Navigating complex organisational politics and unspoken social cues can be challenging. We'll provide clear guidance on stakeholder mapping and communication strategies, and your Senior Consultant will help you interpret nuanced feedback.
2. Dealing with sudden, 'urgent' changes to project scope or priorities can be unsettling. We aim for transparency and will give as much advance notice as possible, and help you re-plan effectively.
3. Networking and informal social interactions might feel draining. We respect individual preferences for social engagement and ensure core work can be done effectively without extensive mandatory socialising.

Key Responsibilities

Experience Levels Responsibilities

1. Level: Mid-Level Professional (L2)
2. Responsibilities: Independently execute data extraction and cleaning for assigned project components. This means writing your own SQL queries and Python scripts to pull data from various sources (like Snowflake or Salesforce) and then getting it into a usable format. Honestly, this can be 60% of the job sometimes.
3. Take ownership of specific analytical tasks within larger projects, like building a customer segmentation model or analysing sales pipeline conversion rates. You'll be responsible for the full lifecycle of that task, from data to initial insights.
4. Develop and maintain dashboards in Tableau or Power BI that track key business metrics. You'll work from existing templates but also propose improvements and build new visualisations as needed.
5. Identify trends, anomalies, and potential business problems within datasets. This isn't just reporting what happened, but starting to ask 'why?' and 'what next?' based on what you see.
6. Propose initial hypotheses and analytical approaches to your Senior Consultant or Project Manager. You'll start to think about how to tackle a problem, not just wait for instructions.
7. Prepare clear, concise summaries of your findings for internal clients. This usually means a few slides in PowerPoint or a well-structured email, focusing on the 'so what' for the business.
8. Provide informal guidance and support to newer team members or interns. You won't have direct reports, but you'll be a helpful resource for those just starting out, answering questions and reviewing basic work.
9. Supervision: You'll have weekly check-ins with your Senior Analytics Consultant to discuss progress, roadblocks, and next steps. For routine tasks, you'll work independently, but for anything new or complex, you'll consult your Senior Consultant for guidance and approval.
10. Decision: You can make routine decisions about your analytical approach (e.g., which SQL joins to use, how to structure your Python script) within established project guidelines. Any decisions impacting project scope, timelines, or client communication need to be discussed and approved by your Senior Consultant or Project Manager. You'll escalate any significant data quality issues or unexpected findings immediately.
11. Success: You're successful when your analytical outputs are consistently accurate, delivered on time, and your insights are clear enough for business stakeholders to understand and act upon. We also look for your ability to proactively spot issues and propose solutions, showing you're thinking beyond just the immediate request.

Decision-Making Authority

• Type: Analytical Approach & Methodology
• Entry: Follows prescribed methods; all choices reviewed.
• Mid: Chooses appropriate methods for routine problems; consults on novel approaches.
• Senior: Designs and validates new methodologies; sets standards for the team.
• Type: Data Source Selection & Validation
• Entry: Uses pre-approved data sources; flags obvious data quality issues.
• Mid: Identifies and validates new data sources; troubleshoots data discrepancies independently.
• Senior: Defines data governance standards; architects data integration strategies.
• Type: Client Communication & Recommendations
• Entry: Drafts communications for review; presents findings under supervision.
• Mid: Communicates routine findings directly to project-level stakeholders; proposes initial recommendations.
• Senior: Leads client presentations; makes final recommendations and manages expectations.
• Type: Project Prioritisation (within your workstream)
• Entry: Executes tasks in order assigned by supervisor.
• Mid: Manages own task queue within a project; flags conflicts to project lead.
• Senior: Prioritises workstreams for junior team members; negotiates deadlines with stakeholders.

Competency Requirements

Foundation Skills (Transferable)

These are the fundamental skills that underpin everything we do. They're not just 'nice-to-haves'; they're essential for navigating the complexities of internal consulting and ensuring your analytical work actually makes an impact.

• Category: Communication & Collaboration
• Skills: Active Listening: Genuinely understanding stakeholder needs, not just hearing them.
• Clear Written Communication: Crafting reports and emails that are easy to read and understand, even for non-technical audiences.
• Verbal Presentation Skills: Explaining complex data clearly and concisely in meetings.
• Teamwork: Collaborating effectively with colleagues on shared projects and supporting others.
• Category: Problem Solving & Critical Thinking
• Skills: Structured Problem Solving: Breaking down ambiguous business problems into manageable analytical questions.
• Logical Reasoning: Drawing sound conclusions from data and identifying potential fallacies.
• Quantitative Acumen: Comfort with numbers, statistics, and interpreting numerical results.
• Root Cause Identification: Moving beyond symptoms to find the underlying issues.
• Category: Personal Effectiveness
• Skills: Time Management: Juggling multiple tasks and deadlines effectively.
• Adaptability: Adjusting to changing priorities and project scopes without losing stride.
• Attention to Detail: Catching errors and inconsistencies in data and reports.
• Initiative: Proactively identifying opportunities or problems and suggesting solutions.

Functional Skills (Role-Specific Technical)

These are the bread-and-butter skills you'll use every day. They're specific methodologies, tools, and areas of knowledge that are crucial for an Analytics Consultant in Internal Consulting.

Technical Competencies

• Skill: Hypothesis-Driven Analysis
• Desc: You'll be able to take a vague business question, like 'Why are our sales down?', and turn it into testable hypotheses. This means figuring out what specific data you need to prove or disprove those ideas, rather than just 'boiling the ocean' with every piece of data you can find.
• Level: Intermediate
• Skill: Business Case Development (Basic)
• Desc: You'll start to understand how to quantify the business impact of your recommendations. This involves rough estimations of ROI or cost savings, moving beyond just statistical significance to actual financial implications. You'll contribute to, but not fully own, the financial models.
• Level: Basic
• Skill: Root Cause Analysis (RCA)
• Desc: You'll use structured frameworks like the '5 Whys' or Fishbone diagrams to dig into why a problem is happening. It's about getting past the symptoms to find the real underlying cause of a business issue.
• Level: Intermediate
• Skill: Data Storytelling & Narrative Design
• Desc: You'll learn how to weave data points, context, and insights into a clear, compelling story for your audience. It's the art of connecting the 'what' (the data) to the 'so what' (the actionable insight) in a way that makes people want to listen and act.
• Level: Intermediate

Digital Tools

• Tool: Tableau / Power BI
• Level: Intermediate
• Usage: Building new dashboards from clean data sources, modifying existing reports, and creating interactive visualisations for internal clients. You'll use filters, calculated fields, and basic joins regularly.
• Tool: SQL (PostgreSQL, T-SQL)
• Level: Intermediate
• Usage: Writing SELECT statements with joins, aggregations, and filtering to extract and prepare data for analysis. You'll be comfortable querying various databases to get the data you need.
• Tool: Python (pandas, NumPy)
• Level: Intermediate
• Usage: Performing basic data cleaning, transformation, and manipulation using the pandas library. You'll use NumPy for numerical operations and often script parts of your analysis workflows.
• Tool: Excel (Power Query, Power Pivot)
• Level: Advanced
• Usage: Mastering VLOOKUP/XLOOKUP, pivot tables, and complex formulas for ad-hoc analysis. You'll use Power Query for repeatable data cleaning and transformation tasks, and potentially Power Pivot for more advanced data modelling.
• Tool: Confluence, Jira, Miro
• Level: Intermediate
• Usage: Documenting your analysis, updating project progress in Jira, participating in sprint planning, and using Miro for brainstorming sessions with your team or clients. Keeping things organised is key.

Industry Knowledge

• Area: Internal Business Operations
• Desc: A solid understanding of how different departments (e.g., Sales, Marketing, Finance, Operations) within our company function and interact. You'll need to know enough to understand their problems and speak their language.
• Area: Basic Statistics
• Desc: Understanding core statistical concepts like averages, medians, standard deviation, correlation, and basic hypothesis testing. You'll know when a trend is meaningful versus just random noise.

Regulatory Compliance Regulations

• Reg: GDPR (General Data Protection Regulation)
• Usage: You'll need to understand the basic principles of GDPR, especially regarding personal data. This means knowing what data you can and cannot use, and how to handle it securely in your analyses. You'll always consult with your Senior Consultant on data privacy implications.
• Reg: Internal Data Governance Policies
• Usage: You'll be expected to follow our company's internal rules for data access, usage, and storage. This includes understanding data classification, who owns which data, and the proper channels for requesting access to sensitive information. It's crucial for maintaining data integrity and security.

Essential Prerequisites

• Proven ability to independently manage and deliver analytical tasks within a project setting (e.g., you've owned a report from start to finish).
• Experience cleaning and preparing messy datasets for analysis using SQL and/or Python.
• Demonstrable experience creating clear, data-driven visualisations and presentations for non-technical audiences.
• A track record of identifying trends or anomalies in data and proposing initial explanations or solutions.
• Strong foundational understanding of business operations, even if in a different industry.

Career Pathway Context

Typically, people joining us as an Analytics Consultant will have spent 2-3 years as an Associate or Junior Analyst, either in an internal team or perhaps a smaller consulting firm. You'll have moved beyond just executing instructions and are now ready to take on more ownership and start interpreting results on your own. You're comfortable with the tools and ready to apply them to real business problems.

Qualifications & Credentials

Emerging Foundation Skills

• Skill: Prompt Engineering & LLM Integration (for analysis)
• Why: Frankly, competitors are already using tools like ChatGPT and Claude to draft reports in minutes that used to take hours. Analysts who figure out how to effectively 'talk' to these AI models will be significantly more productive. It's not about replacing you, but augmenting you.
• Concepts: [{'concept_name': 'Context Windows & Token Limits', 'description': "Understanding how much information an AI model can 'remember' and process at once, and how to manage that for complex analytical queries."}, {'concept_name': 'Temperature Settings', 'description': 'Knowing when to ask for creative, exploratory responses versus precise, factual outputs from an LLM.'}, {'concept_name': 'RAG (Retrieval Augmented Generation)', 'description': "Learning how to connect LLMs to our internal, proprietary data sources to get accurate, context-specific insights without 'hallucinations'."}, {'concept_name': 'Output Validation & Hallucination Detection', 'description': 'Developing a critical eye to verify AI-generated outputs, understanding that they can sometimes make things up.'}]
• Prepare: This week: Set up GitHub Copilot (or similar) and use it for every piece of code you write, even small snippets.
• This month: Experiment with ChatGPT or Claude to draft initial summaries of your analytical findings, then refine them.
• Month 2: Explore how to connect an LLM to a small, non-sensitive internal dataset (e.g., a CSV) to ask questions and get insights.
• Month 3: Document your productivity gains and share your learnings with the team in a quick 15-minute 'lunch and learn' session.
• QuickWin: Start using AI to draft email summaries, brainstorm analytical approaches, or even generate code comments today. No approval needed, immediate benefit.
• Skill: Basic Machine Learning Concepts (Practical Application)
• Why: More and more business problems are being tackled with ML, even at a basic level. Understanding the fundamentals will help you identify opportunities for more advanced analysis and better communicate with data science teams. You won't be building complex models from scratch, but you'll know what's possible.
• Concepts: [{'concept_name': 'Supervised vs. Unsupervised Learning', 'description': 'Knowing the difference and when to apply each (e.g., predicting a value vs. finding clusters).'}, {'concept_name': 'Regression & Classification Basics', 'description': 'Understanding these common model types and their business applications (e.g., predicting sales, classifying customer churn).'}, {'concept_name': 'Feature Engineering (Basic)', 'description': 'How to prepare and select the right data inputs for a model to perform well.'}, {'concept_name': 'Model Evaluation Metrics', 'description': 'Understanding what metrics like accuracy, precision, recall, or RMSE actually mean in a business context.'}]
• Prepare: This week: Read a beginner's guide to machine learning for business professionals.
• This month: Complete an online course on basic ML concepts using Python (e.g., a Datacamp or Coursera module).
• Month 2: Try to apply a simple regression model to one of your existing datasets (e.g., predicting customer lifetime value).
• Month 3: Present a 'lessons learned' from your ML experiment to your Senior Consultant, focusing on business implications.
• QuickWin: Start by understanding the difference between correlation and causation, and how that impacts any predictive analysis you might do. It's a fundamental concept that's often overlooked.

Advancing Technical Skills

• Skill: Advanced SQL & Data Modelling
• Why: As our data landscape grows, you'll need to pull more complex data more efficiently. This means moving beyond basic joins to understanding window functions, CTEs (Common Table Expressions), and how data is structured in our enterprise platforms. It's about writing cleaner, faster, more maintainable queries.
• Concepts: [{'concept_name': 'Window Functions (e.g., ROW_NUMBER, LAG, LEAD)', 'description': 'Performing calculations across a set of table rows that are related to the current row.'}, {'concept_name': 'Common Table Expressions (CTEs)', 'description': 'Organising complex queries into readable, reusable sub-queries.'}, {'concept_name': 'Indexing & Query Optimisation', 'description': 'Understanding how to make your queries run faster and reduce database load.'}]
• Prepare: This week: Identify one recurring, slow-running query you write and research how to optimise it.
• This month: Complete an online module specifically on advanced SQL concepts (window functions, CTEs).
• Month 2: Refactor an existing complex query using CTEs for better readability and performance.
• Month 3: Get a peer to review your optimised query and explain the improvements you made.
• QuickWin: Start using `EXPLAIN ANALYZE` (or similar for your database) on your queries to understand where the bottlenecks are. It's a simple command that gives huge insights.
• Skill: Advanced Data Visualisation & Dashboard Performance
• Why: As you build more dashboards, you'll need to make them not just pretty, but truly performant and insightful. This means understanding how to optimise data sources, use advanced calculations, and design for user experience, especially for larger datasets. Nobody wants a slow dashboard.
• Concepts: [{'concept_name': 'LOD (Level of Detail) Expressions (in Tableau)', 'description': "Performing calculations at a specific level of aggregation, independent of the visualisation's level of detail."}, {'concept_name': 'Dashboard Performance Optimisation', 'description': 'Techniques to make dashboards load faster and respond more quickly, especially with large datasets.'}, {'concept_name': 'Advanced Interactivity & User Experience (UX)', 'description': 'Designing dashboards that are intuitive, easy to navigate, and provide clear answers to user questions.'}]
• Prepare: This week: Review one of your existing dashboards and identify 2-3 areas where performance could be improved.
• This month: Complete an advanced Tableau or Power BI course focusing on performance and complex calculations.
• Month 2: Implement 1-2 performance improvements on a live dashboard and measure the impact.
• Month 3: Present your findings on dashboard optimisation to the team, sharing best practices.
• QuickWin: Always hide unused fields in your data sources and minimise the number of filters on your dashboards. Small changes, big impact.

Education Requirements

• Level: Minimum
• Req: A Bachelor's degree (or equivalent) in a quantitative field such as Mathematics, Statistics, Computer Science, Economics, Business Analytics, or a related discipline.
• Alts: We're pragmatic. If you've got significant, demonstrable experience (5+ years) in a highly analytical role, with a strong portfolio of projects, that could absolutely count as equivalent. Show us what you can do.
• Level: Preferred
• Req: A Master's degree in a quantitative field, or a specific qualification in Business Analytics.
• Alts: Specialised certifications in data science, business intelligence, or specific analytical tools (like advanced Tableau or Python for Data Analysis) can also give you an edge.

Experience Requirements

You'll need roughly 2-5 years of hands-on experience in a data analysis, business intelligence, or junior consulting role. This isn't your first rodeo with data. We're looking for someone who has independently owned analytical tasks from start to finish, not just executed instructions. You should have a proven track record of using SQL and a visualisation tool (Tableau/Power BI) to deliver insights, and ideally, some experience with Python for data manipulation. Experience working directly with business stakeholders to understand their needs is a big plus.

Preferred Certifications

• Cert: Tableau Desktop Specialist / Certified Associate
• Prod: Tableau
• Usage: Demonstrates a solid understanding of Tableau's capabilities and best practices for data visualisation, which is a core tool for us.
• Cert: Microsoft Certified: Data Analyst Associate (Power BI)
• Prod: Microsoft
• Usage: Shows proficiency in Power BI, another key visualisation tool, and an understanding of data modelling concepts within that ecosystem.
• Cert: Python for Data Science (e.g., IBM, Google)
• Prod: Various (Coursera, edX)
• Usage: Validates your skills in using Python libraries like pandas and NumPy for data manipulation and analysis, which is becoming increasingly important.

Recommended Activities

• Actively participate in online data communities (e.g., Kaggle, Stack Overflow) to keep your skills sharp and learn from others.
• Attend industry webinars or virtual conferences on analytics trends and new tools.
• Take on a 'stretch' project within your current role that pushes you to learn a new analytical technique or tool.
• Seek out opportunities to present your findings to larger or more senior audiences to refine your communication skills.
• Regularly engage in peer code reviews, both giving and receiving feedback, to improve your coding standards and learn new approaches.

Career Progression Pathways

Entry Paths to This Role

• Path: Associate Analytics Consultant (Internal)
• Time: 2-3 years
• Path: Data Analyst (External Company)
• Time: 2-4 years
• Path: Junior Consultant (External Consulting Firm)
• Time: 2-3 years

Career Progression From This Role

• Pathway: Senior Analytics Consultant (L3)
• Time: 3-5 years

Long Term Vision Potential Roles

• Title: Lead Analytics Consultant (L4)
• Time: 5-8 years from now
• Title: Principal Analytics Advisor (L5)
• Time: 8-12 years from now
• Title: Director, Analytics & Strategic Insights (L6)
• Time: 12-16 years from now
• Title: Chief Analytics Advisor (L7)
• Time: 16-20+ years from now

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.