United Kingdom · Learning and development · Senior (5-8 years)

Senior Global Learning Analytics Manager

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 bandSenior (5-8 years)
Direct reportsNo direct reports
Reports toGlobal Learning Analytics Manager Manager
UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Senior Learning Data Analyst · Learning & Development Insights Lead · People Analytics Specialist (L&D Focus)

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

Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 10 standards

Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 2 of 10 standards

NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 6 of 10 standards

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 4 of 10 standards

Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 3 of 10 standards

Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF) Highfield Qualifications · Level 3 · 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

Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England

Data Analytics Primer NOCN Level 4 certificate for Data Analysts

Data Analysis and Visualisation OTHM Level 7 Diploma in Data Science

Data Analysis Highfield Level 3 Diploma for Team Leaders and Supervisors (RQF)

The future skills this job leans on

AI Fluency working with AI as a thinking partner

Data Fluency turning numbers into a decision

Automation Fluency building AI into how work runs

Systems Fluency seeing the whole picture

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Your Plan Illustration

Built for Senior Global Learning Analytics Manager

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

Data Analytics Pearson Education Ltd · covers 4 of 10 standards Level 5
Data Analytics Primer NOCN · covers 6 of 10 standards Level 4
Data Analysis and Visualisation OTHM Qualifications · covers 4 of 10 standards Level 7
Data Analysis Highfield Qualifications · covers 2 of 10 standards Level 3

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 Senior Global Learning Analytics Manager?

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

Prompt Engineering & LLM Integration for Analytics
Advanced Causal Inference & Quasi-Experimental Design

Why each one is rising

The future skills this job leans on

AI Fluencyworking with AI as a thinking partner
Data Fluencyturning numbers into a decision
Automation Fluencybuilding AI into how work runs
Systems Fluencyseeing the whole picture

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 Senior Global Learning Analytics Manager 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

This job analyses data from learning systems, HR platforms and surveys to see what works in global learning programmes and what does not. It means turning complex data into clear findings that help leaders make decisions about how people develop across the world. The work is a bit like detective work, with data points as clues and better learning outcomes as the result.

4What you'd actually use

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

Power BI / Tableau Advanced

Designing, building, and deploying complex, interactive dashboards from scratch. You'll use advanced DAX or LOD calculations and manage multiple data sources to create compelling visualisations for leadership.

LMS/LXP APIs (e.g., Cornerstone, Workday Learning, Degreed) Intermediate

Extracting data programmatically via REST APIs to get beyond standard UI reports. You'll understand the nuances of xAPI/cmi5 data structures and how to pull the right information.

HRIS Reporting (Workday Report Writer / SuccessFactors BIRT) Intermediate

Creating custom reports and calculated fields from our HRIS to get employee demographics, performance ratings, and job roles. You'll understand the core HCM data objects and how they relate to learning data.

SQL (PostgreSQL, T-SQL) Advanced

Writing complex multi-join queries, Common Table Expressions (CTEs), and window functions to transform raw data from various sources into a clean, usable format for analysis. This is your bread and butter for data manipulation.

Python (pandas, NumPy, scikit-learn) Advanced

Using scripts for advanced data cleaning, transformation, statistical analysis, and building predictive models. You'll be comfortable with these libraries for deeper analytical work.

Qualtrics / SurveyMonkey Intermediate

Implementing complex survey logic, branching, and automated triggers for feedback collection. You'll also use these tools to analyse text data from open-ended feedback, not just quantitative scores.

Jira / Confluence Intermediate

Creating project plans, managing sprint backlogs for analytical projects, and writing technical documentation for data models and methodologies. You'll keep our work organised and transparent.

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 choice	Coming in	Where you are now	The step above
Project Methodology & Tool Selection	Proposes options, but requires full approval from a senior analyst or manager.	Chooses methodology for routine tasks; consults manager for complex or novel approaches. Selects standard tools.	Full authority to define analytical methodology and select technical tools (e.g., Python libraries, specific Power BI features) within project scope. Informs manager of choices.
Data Interpretation & Recommendations	Presents findings and potential interpretations; recommendations are drafted with guidance and require full review.	Independently interprets data for routine requests and drafts recommendations; manager reviews for strategic implications.	Independently interprets complex data, forms actionable recommendations, and presents directly to L&D leadership or HRBPs. Manager is informed, not necessarily approving every detail.
Stakeholder Communication Strategy	Communicates with immediate team and supervisor; all external communication drafted with guidance.	Communicates directly with internal clients on project updates and data requests; manager reviews key messages.	Designs and executes communication strategy for analytical findings to senior internal stakeholders (e.g., VPs, Directors). Consults manager on highly sensitive or political communications.
Data Quality Issue Resolution	Identifies data quality issues and reports them to a senior team member.	Investigates root causes of data quality issues and proposes solutions; manager approves implementation.	Leads investigation and resolution of complex data quality issues, working with IT or vendors. Recommends system improvements and implements approved fixes. Informs manager of progress and impact.
Budget for Small Tools/Resources	No budget authority; requests resources through supervisor.	Can request small, pre-approved resources (e.g., a specific dataset purchase up to £500) with manager approval.	Recommends and justifies expenditure for project-specific tools or data sources up to £10K; requires manager's final sign-off.

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.

Project Completion & Impact

Successfully delivering complex analytical projects that address key L&D or business questions.

Target · 85% of assigned projects completed on time and delivering actionable insights.

Delivered a skills gap analysis that led to a £150,000 reallocation of L&D budget to critical emerging skills, completed within the agreed 10-week timeline.

Insight Adoption Rate

How often your analytical recommendations are actually taken up and acted upon by L&D leadership or business partners.

Target · 60% of key recommendations result in a measurable change in L&D strategy or programme design.

Your analysis showed that a particular compliance training wasn't reducing incidents. L&D then redesigned the programme based on your findings, leading to a 10% drop in reported incidents.

Mentee Development & Retention

The growth and engagement of any junior analysts you're informally mentoring.

Target · At least one mentee shows significant progress (e.g., takes on more complex tasks, improves code quality) within 12 months, and remains with the team.

Helped a junior analyst improve their SQL skills to the point where they could independently build a new data extract, and they expressed high job satisfaction.

Data Quality Improvement

Identifying and helping to resolve underlying data quality issues across our learning and HR systems.

Target · Reduce identified critical data errors by 20% across key L&D datasets annually.

Worked with IT to fix an issue where 5% of learning completion data was not syncing correctly from the LMS, ensuring more accurate reporting for all future analyses.

Stakeholder Trust & Influence

Being seen as a credible, go-to expert who can be relied upon for objective, well-communicated insights.

You're proactively invited to strategic L&D planning sessions. Leaders ask your opinion before making big programme decisions. Your presentations are clear, concise, and lead to productive discussions, not just more questions. People actually listen to your data stories.

Proactive Problem Solving

Identifying potential data issues or analytical needs before they become urgent problems, and proposing solutions.

You flag a potential data discrepancy in the LMS before anyone else notices. You suggest a new way to measure learning transfer for a programme that's about to launch. You don't just answer the question asked
you anticipate the next two questions.

Analytical Rigour & Innovation

Applying sound statistical methods and exploring new analytical approaches to solve complex L&D questions.

Your analyses consistently stand up to scrutiny. You're experimenting with new modelling techniques (e.g., predictive analytics for attrition). You're not just running the same reports
you're pushing the boundaries of what we can learn from our data.

Mentorship & Knowledge Sharing

Effectively guiding and developing junior team members, and sharing your expertise with the wider L&D team.

Junior analysts come to you for advice. You're running informal workshops on data best practices. You're contributing to our team's documentation and helping others understand complex methodologies. People feel comfortable asking you 'silly' questions.

7Would you like it

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

What people enjoy

Solving Complex Puzzles

You'll be faced with ambiguous questions like 'Does our new onboarding programme actually reduce early attrition?' and you'll get to design the entire analytical approach to answer it. It's like a new riddle every week.

Spending a full day wrestling with disparate datasets to create a single, unified view of learner engagement, and finally seeing the pieces click into place.

Driving Tangible Impact

Your analyses won't just sit in a report; they'll directly influence how we spend our L&D budget, design new programmes, and ultimately help our employees develop. You'll see your work make a difference.

Presenting data that convinces leadership to invest in a new coaching programme for managers, and then seeing positive shifts in team performance metrics six months later.

Continuous Learning & Mastery

The world of data and learning science is always evolving. You'll be constantly learning new statistical techniques, exploring new data visualisation tools, and deepening your understanding of human behaviour and learning theory.

Spending a few hours each week experimenting with a new Python library for text analysis to see if it can improve how we categorise open-ended feedback.

What frustrates people

The 'Causality vs. Correlation' Battle: Constantly explaining to senior leaders that just because sales went up after a training programme doesn't mean the training caused it. You'll spend half your life trying to isolate variables, and they'll still ask for a single ROI number.
Garbage In, Garbage Out: Your analysis is only as good as the data. You will spend a significant chunk of your time cleaning, merging, and validating messy data from the LMS, HRIS, and a dozen other systems that were never designed to talk to each other.
The Quest for the 'One ROI Number': Leadership will relentlessly ask for a single, simple ROI figure for all L&D spending. You will relentlessly explain why this is a methodological nightmare and a misleading metric, but they'll still ask for it next quarter. Every quarter.
Data Silos & Gatekeepers: Begging the Finance department for performance data or the Sales Ops team for quota attainment data is a full-time job involving politics, horse-trading, and endless meetings. People guard their data like dragons.
'Flavor of the Month' Metrics: A new CHRO arrives and suddenly everything needs to be measured in terms of 'Learning Velocity' or some other buzzword, forcing you to abandon your carefully constructed analytics roadmap and start from scratch.
LMS Reporting Sucks: The built-in reporting module of nearly every major LMS is inflexible and inadequate, forcing you to build complex, brittle workarounds just to get basic data out. It’s a constant battle.

What this role does not give you

A perfectly clean, ready-to-analyse dataset handed to you on a plate.
A quiet, predictable work environment where priorities never shift.
Immediate, universal adoption of every single insight you produce.
A role where you only build models and never have to explain them to non-technical people.

8Who you work with

This role directly shapes our global L&D strategy by providing evidence-based recommendations. You'll help us optimise our learning investment, identify critical skill gaps, and prove the tangible business value of our people development efforts. Essentially, you'll be giving us the data-driven confidence to make big decisions about our most valuable asset: our people.

Inside the business

Director of L&D
HR Business Partners (HRBPs)
Talent Acquisition Lead
IT Data Engineering Team
Product Team (for learning platforms)
Finance Business Partners

Outside the business

Learning content vendors
Learning platform providers (e.g., Cornerstone, Workday Learning)
External research partners

9What you need before you start

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

At least 5 years of hands-on experience in a data analytics role, with a significant portion focused on HR, People, or Learning analytics.
Proven ability to lead analytical projects from conception to delivery, including stakeholder management and presentation of findings.
Advanced proficiency in SQL for complex data manipulation and extraction (you should be able to write CTEs and window functions in your sleep).
Strong experience with at least one major BI tool (Power BI or Tableau) for building complex, interactive dashboards.
Demonstrable experience with Python (pandas, NumPy) for data cleaning, transformation, and statistical analysis.
A solid understanding of statistical methods (e.g., regression, hypothesis testing, A/B testing) and when to apply them.
Experience working with data from HRIS (e.g., Workday, SAP SuccessFactors) and/or LMS/LXP platforms (e.g., Cornerstone, Degreed).

10What to practise next

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

Enterprise BI Governance & Performance Optimisation

As our dashboards grow, performance issues and inconsistent reporting become a real headache. You'll need to understand how to keep our BI ecosystem healthy and reliable.

Data Model Optimisation (Star Schema) · DAX/LOD Optimisation · Row-Level Security (RLS) · Deployment Pipelines & Version Control · Monitoring & Alerting

This week: Review the performance of one of our existing complex dashboards and identify potential bottlenecks.
This month: Research best practices for Power BI Premium or Tableau Server governance and data model design.
Month 2: Take an advanced course on DAX or Tableau performance optimisation.
Month 3: Propose and implement a small-scale improvement to an existing dashboard's performance or security.
Ongoing: Actively participate in discussions with our IT Data Engineering team about BI infrastructure.

Quick win: Start using the Power BI Performance Analyser or Tableau's built-in performance recording to understand where your dashboards are spending their time. Small tweaks can make a big difference.

MLOps for Learning Models

Building a predictive model is one thing; deploying it, monitoring its performance, and retraining it automatically is another. As we move towards more advanced analytics, operationalising these models will be key.

Model Versioning & Experiment Tracking · Automated Retraining Pipelines · Model Monitoring & Alerting · Containerisation (Docker) · Cloud ML Platforms (Azure ML, AWS SageMaker)

This week: Read an introductory article on MLOps and its importance in data science.
This month: Explore Docker and containerise a simple Python script you've already written.
Month 2: Take an online course on MLOps or a cloud-specific ML platform (e.g., Azure ML Fundamentals).
Month 3: Work with IT Data Engineering to understand our current MLOps capabilities and identify a pilot project for a learning model.
Ongoing: Experiment with open-source MLOps tools like MLflow or Kubeflow in a sandbox environment.

Quick win: Start incorporating structured experiment tracking (even just in a spreadsheet) for your predictive models, noting parameters, data versions, and performance metrics. This builds good MLOps habits early.

11Staying current once you are in

What people here do to keep up

Regularly attending industry conferences or webinars on People Analytics, Learning Technologies, or Data Science (e.g., People Analytics World, Learning Technologies Conference).
Participating in online courses or bootcamps to deepen skills in advanced statistics, machine learning, or cloud-based data platforms (e.g., Coursera, edX, DataCamp).
Contributing to relevant open-source projects or maintaining a public portfolio of analytical work (e.g., GitHub, Tableau Public).
Reading academic journals or industry publications focused on learning science, organisational psychology, or HR research to stay current with best practices.
Joining professional networks or communities of practice for learning analytics or people analytics professionals to share insights and learn from peers.

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.

Prompt Engineering & LLM Integration for Analytics

Competitors are already using Large Language Models (LLMs) to draft reports in 10 minutes that used to take 2 hours. Analysts who figure this out will outproduce peers 3:1. This isn't just about ChatGPT; it's about integrating these tools into your workflow.

Context Windows & Token Limits
Temperature Settings for Tasks
RAG (Retrieval Augmented Generation)
Output Validation & Hallucination Detection
Prompt Chaining for Complex Analysis

Advanced Causal Inference & Quasi-Experimental Design

Leaders are tired of 'correlation isn't causation' caveats. The demand for proving causal impact of learning programmes is only growing. Simply showing a link isn't enough; you'll need to demonstrate genuine impact.

Difference-in-Differences (DiD)
Regression Discontinuity Design (RDD)
Propensity Score Matching (PSM)
Instrumental Variables (IV)
Synthetic Control Methods

13What you’ll use

What you’ll use

Skills this role draws on

Technical

Kirkpatrick Model of Evaluation (Level 1-4)
Learning Transfer Analysis
Skills Taxonomy & Ontology Design
Predictive Modelling for Learning Outcomes
Learning Experience Data Governance (xAPI/cmi5)

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
Learning Data Analyst (Level 002)
3-5 years
Skills to master
- Independently handling ad-hoc data requests, building reliable dashboards, conducting descriptive analysis, and beginning to identify patterns in learning data. You'll have mastered SQL and a BI tool.
You're ready to move on when
- Consistently delivers accurate and timely analyses without heavy supervision.
- Proactively identifies data quality issues and proposes solutions.
- Begins to translate basic data findings into simple, actionable insights for L&D stakeholders.
- Shows initiative in learning new analytical techniques and tools.
2
Data Scientist (from another domain)
5-7 years
Skills to master
- Transferring core data science skills (e.g., machine learning, statistical modelling, programming) to the unique context of learning and HR data. You'll need to quickly learn about learning science and HR processes.
You're ready to move on when
- Demonstrable experience building and deploying predictive models in previous roles.
- Strong programming skills in Python or R for statistical analysis.
- A genuine interest in human behaviour, learning, and talent development.
- Ability to quickly grasp new domain knowledge and apply analytical frameworks to it.
3
HR Analyst / People Analyst
4-6 years
Skills to master
- Deepening technical skills in SQL and Python, moving beyond standard HR reporting to more advanced statistical analysis and predictive modelling. Specialising in the learning domain.
You're ready to move on when
- Strong understanding of HR data structures and common HR metrics.
- Experience with HRIS reporting and data extraction.
- A clear desire to specialise in learning and development analytics.
- Proactive in learning advanced analytical techniques and tools.

15Where this role leads

The long view: This role can lead to Director, Learning & Workforce Analytics, or VP, Talent & Organisational Analytics.

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 Senior Global Learning Analytics Manager is actually changing.

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 Data Analytics, worked against Senior Global Learning Analytics Manager.

Data Analytics Level 5

Applied to your work in Senior Global Learning Analytics Manager

The objective of this unit is to enable learners to understand and apply data analytics techniques for decision-making. Learners will be able to apply descriptive, predictive, and prescriptive analytic methods, utilising statistical methods, to convert raw data into actionable insights and determine the best course of action.

How the thinking builds

1 Remember
2 Understand
3 Apply
4 Analyse
5 Evaluate
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.

Start my plan, free

See Your Progress Grow Illustration

Senior Global Learning Analytics Manager

Kirkpatrick Model of Evaluation (Level 1-4)
Learning Transfer Analysis
Skills Taxonomy & Ontology Design
Predictive Modelling for Learning Outcomes
Learning Experience Data Governance (xAPI/cmi5)

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

Senior Global Learning Analytics Manager is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

Lead Learning Analytics Consultant (Level 004)
3-5 years

One level up (L3 to L4)
- Enterprise Data Architecture for L&D Designing ETL/ELT pipelines and data warehousing solutions.
- Advanced Statistical Consulting Acting as the go-to expert for complex statistical challenges across the business.
- Vendor Management (Data Focus) Evaluating and managing data-related vendors and partnerships.
- Budget Management (up to £500K) Owning the budget for analytical tools, data sources, and team resources.
Global Learning Analytics Manager Manager (Level 005)
5-7 years

Two levels up (L3 to L5)
- Advanced Data Governance Strategy Defining and implementing enterprise-wide data governance for people data.
- Strategic Platform Evaluation Making decisions on enterprise BI and data warehousing platforms.
- External Representation Representing the organisation at industry conferences and thought leadership events.
- Innovation Portfolio Management Overseeing a portfolio of analytical innovation projects.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a big chunk of your week is probably spent on tasks that, while necessary, aren't the most exciting. Imagine getting back 10-15 hours every week to focus on the really meaty, strategic analytical challenges. That's what AI can do for you in this role.

We're not talking about replacing your brain; we're talking about giving you a seriously powerful assistant. Our AI Productivity Hub is designed to help you automate the tedious, speed up your research, and even help you draft those tricky executive summaries. Think of it as your secret weapon to become an even more impactful Senior Learning Analytics Manager.

Automated Data Harmonization

Use AI scripts to automatically clean, map, and merge disparate datasets from the LMS (Cornerstone), HRIS (Workday), and engagement surveys (Qualtrics). It'll flag anomalies for your human review, but it'll do the grunt work of getting your data ready for analysis in a fraction of the time.

Thematic Feedback Analysis

Feed thousands of open-ended comments from course feedback surveys ('smile sheets') into an LLM. It'll instantly identify and quantify key themes, sentiment, and actionable suggestions, replacing hours of manual reading and categorisation. You'll get insights from qualitative data much faster.

Pre-Meeting Research Synthesis

Need to quickly get up to speed on a new topic for a high-stakes meeting? Ask an AI assistant to 'Summarise the top 5 academic papers from the last 3 years on measuring learning transfer for soft skills'. You'll get instant domain expertise, saving you hours of digging through journals.

Executive Summary Drafting

After creating a complex dashboard in Tableau, paste screenshots or key data points into an LLM. Prompt it to 'Write a 3-bullet point executive summary for a CHRO explaining the key insights and recommended actions from this data.' It'll give you a solid first draft, freeing you up to refine the message.

Common questions

How do you become a Senior Global Learning Analytics Manager?

Common routes in include Learning Data Analyst (3-5 years), Data Scientist (from another domain) (5-7 years) and HR Analyst / People Analyst (4-6 years). Times vary with prior experience.

Where can a Senior Global Learning Analytics Manager progress to?

This role can lead on to Lead Learning Analytics Consultant (3-5 years) and Global Learning Analytics Manager Manager (5-7 years), depending on the skills you build.

What new skills matter most for a Senior Global Learning Analytics Manager?

Increasingly, Prompt Engineering & LLM Integration for Analytics and Advanced Causal Inference & Quasi-Experimental Design. These are the skills our record of this role lists as growing.

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 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.

Senior Global Learning Analytics Manager

Where should we send it?

Two short quizzes about you

Will AI change your work as a Senior Global Learning Analytics Manager?

What's rising in its place

Prompt Engineering & LLM Integration for Analytics

Advanced Causal Inference & Quasi-Experimental Design

Skills this role draws on

Technical

How you actually get there, here

Learning Data Analyst (Level 002)

Data Scientist (from another domain)

HR Analyst / People Analyst

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

Roles this leads to

One to one, on the work you already do

Everything else the record holds

The career path, and where it branches

Lead Learning Analytics Consultant (Level 004)

Global Learning Analytics Manager Manager (Level 005)

Where AI is starting to help

Automated Data Harmonization

Thematic Feedback Analysis

Pre-Meeting Research Synthesis

Executive Summary Drafting

Common questions

Want a read on where you stand?