> ## Documentation Index
> Fetch the complete documentation index at: https://support.metaview.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Talent Mapping

> Guide to advanced best practices using Sourcing.

Build a structured view of everyone working in a particular function at a target company, organized by team, seniority, and location.

Talent Mapping is designed for understanding a company’s organization and talent distribution. It is company mapping, not a candidate search.

## When to use it

Use Talent Mapping when you want to understand how a target company is structured and where its talent is concentrated.

It is especially useful when:

* researching a competitor or target company;
* identifying the teams and locations where relevant talent sits;
* understanding the seniority mix within a function;
* finding senior leaders or notable individuals;
* preparing a company-specific sourcing strategy;
* identifying potential organizational gaps or underrepresented teams;
* tracking talent across acquired companies, subsidiaries, or former company names.

For example:

> Create a talent map of people in Product at Stripe. Flag anyone who has founder experience.

## What you get

The task produces two outputs:

**`talent_map.csv`** A structured file with one row per person, including their title, function, seniority, location, profile URL, and evidence that they currently work at the company.

**HTML overview** A clean, scannable summary of the company’s organization, including:

* headcount by function;
* headcount by location;
* seniority distribution;
* key leaders and notable individuals;
* company aliases and office locations;
* likely gaps or uncertainty in the map.

The HTML overview is designed to give a recruiter or hiring leader a useful picture of the company without needing to inspect every row in the CSV.

## How it works

Start by replacing:

**Company**

with the target company.

Replace:

**Functions**

with one or more functions or departments to map, such as:

* Product;
* Engineering;
* Sales;
* Marketing;
* Finance;
* People;
* Operations;
* Customer Success.

You can also replace:

**Optional Flag**

with a characteristic you want highlighted, such as:

* founder experience;
* previous employment at a competitor;
* AI or machine-learning experience;
* experience at an early-stage startup;
* prior leadership roles;
* recent promotion;
* specific product or industry experience.

For example:

> Create a talent map of people currently working at Stripe across Product and Design. Flag anyone who has previously founded a company.

The task first identifies alternate ways the company may appear on professional profiles. These may include:

* shortened company names;
* former names;
* parent-company names;
* subsidiaries;
* acquired brands;
* regional entities;
* major business units.

It also identifies likely office locations so the search can account for people who list a local office or subsidiary rather than the main company name.

The map is then built systematically across company-name variants, functions, seniority levels, and locations. Results are deduplicated before the final files are produced.

## Talent Mapping versus candidate search

Talent Mapping starts with the company and attempts to map the relevant organization as comprehensively as possible.

A candidate search starts with a hiring brief and attempts to identify people who are likely to fit a particular role.

This distinction affects how uncertain profiles are handled.

In a candidate search, a marginal or ambiguous profile may be excluded to preserve relevance. In a talent map, it can be more useful to retain a plausible match so the organizational picture is not artificially incomplete.

The task therefore errs slightly toward inclusion, while still applying basic checks.

## How profiles are validated

Professional-profile information should be treated as self-reported and potentially incomplete or outdated.

Before including someone, the task checks whether:

* the current title and role appear plausible;
* the company name matches a known alias, subsidiary, or acquired brand;
* the person appears to be a current direct employee;
* the profile does not primarily describe them as a consultant, vendor, advisor, or external contractor.

The map includes an evidence field explaining why the person appears to work at the company.

Examples include:

* “Current title states Product Manager at Stripe since 2024.”
* “Profile lists employment at Stripe Payments Europe.”
* “Current role shown at acquired company now operating as a Stripe subsidiary.”

These checks improve reliability, but the resulting map should still be treated as a best-effort view rather than an official organization chart.

## Understanding the CSV

The CSV contains one row per person and the following fields:

**Name** The person’s name as shown on their profile.

**Current title** Their current self-reported title.

**Function / department** A normalized function such as Product, Engineering, Sales, or Operations.

**Seniority level** A normalized level such as Individual Contributor, Manager, Director, Vice President, or Executive.

**Location** The person’s visible profile location.

**Current company label shown on profile** The exact company name or entity shown on the source profile.

**Profile URL** A link to the source profile.

**Evidence they currently work there** A concise explanation supporting inclusion.

**Optional flag + evidence** Whether the person matches the requested flag and the evidence supporting it.

The function and seniority fields are normalized to make the file easier to filter, group, and analyze. They may therefore differ slightly from the wording used in the person’s original title.

## Understanding the HTML overview

The HTML overview summarizes the final deduplicated map.

### Company snapshot

This includes the target company, discovered aliases or subsidiaries, and known office locations used in the search.

Review this section first. Missing aliases or office locations can result in gaps elsewhere in the map.

### Headcount by function

This shows how the mapped population is distributed across functions or departments.

The counts represent people found in the map, not the company’s official internal headcount.

They are most useful for comparing relative concentration, such as whether the company appears to have a much larger Engineering organization than Product organization.

### Headcount by location

This shows where the mapped employees are based.

Location is usually derived from self-reported profile information and may represent a city, metropolitan area, region, or country. Remote employees may list their home location rather than the office they support.

### Seniority distribution

This summarizes the balance between individual contributors, managers, directors, vice presidents, and executives.

Use this to understand whether the function appears:

* leadership-heavy;
* relatively flat;
* manager-dense;
* dominated by individual contributors;
* concentrated around a small number of senior leaders.

### Key people

This highlights senior leaders and notable individuals, grouped by function.

This section is intended for navigation and orientation. It should not be treated as confirmation of internal reporting lines unless those relationships are explicitly visible.

### Coverage summary

This explains:

* how many unique people were found;
* which functions or locations appear well covered;
* where results appear sparse;
* which aliases, locations, or employee types may remain uncertain.

## How to improve the input

The most useful additional instructions affect the scope of the map.

### Define the function carefully

Broad function labels can produce significantly different maps.

For example:

> Map everyone in Product.

may include product managers, product operations, product analytics, product strategy, and product leadership.

A narrower version might be:

> Map Product Management only. Exclude Product Design, Product Marketing, Product Analytics, and Product Operations.

Use broader instructions when you are trying to understand the whole organization. Use narrow instructions when you need a clean list for a particular sourcing motion.

### Specify how to handle adjacent teams

Some roles sit across multiple functions.

For example:

* solutions engineers may sit within Sales or Engineering;
* product analysts may sit within Product or Data;
* developer relations may sit within Marketing or Engineering;
* product operations may sit within Product or Operations.

State whether these should be included:

> Include Product Operations and Product Analytics, but keep them as separate sub-functions.

### Add relevant company aliases

The task will attempt to discover company-name variants automatically, but known context can improve coverage.

For example:

> Include employees whose profiles list Afterpay, Clearpay, or Block where their role appears to be part of the Afterpay organization.

This is especially useful for companies with:

* major acquisitions;
* several regional legal entities;
* a recent rebrand;
* multiple well-known product brands;
* business units that retain separate identities.

### Define location scope

Clarify whether you want:

* the entire global function;
* specific countries;
* one office;
* a practical commuting market;
* remote employees;
* employees attached to a regional entity.

For example:

> Map the global Product organization, but separate London, New York, San Francisco, and remote employees in the overview.

### Use a meaningful optional flag

A flag is most useful when it supports a specific sourcing or research question.

Good examples include:

> Flag anyone with founder experience.

> Flag anyone who previously worked at Datadog.

> Flag anyone promoted within the last two years.

> Flag anyone with both Product and Engineering experience.

> Flag anyone who joined from a Series A–C startup.

Avoid broad flags such as “high quality” or “strong candidate.” These require subjective assessment and are less reliable from public-profile information.

## Useful ways to adapt the task

### Map an entire function globally

> Create a talent map of everyone currently working in Product at Stripe globally. Separate Product Management, Product Operations, and Product Analytics. Flag anyone with founder experience.

Use this to understand the overall structure and leadership hierarchy.

### Map one regional organization

> Create a talent map of people in Sales at Stripe across the UK and Ireland. Break down the map by Enterprise, Mid-Market, SMB, Partnerships, and Sales Engineering where visible.

Use this when the hiring strategy is tied to a particular market or office.

### Map a specialized team

> Create a talent map of people working on machine learning, applied AI, or data science at Canva. Include relevant employees whose formal function is Engineering, Product, or Research.

Use this when the target group cuts across traditional departments.

### Identify competitor alumni

> Create a talent map of Product leaders at Stripe. Flag anyone who previously worked at Adyen, Checkout.com, Block, or PayPal.

Use this to identify movement between a set of related companies.

### Understand an acquired organization

> Create a talent map of people currently working within Microsoft’s GitHub organization. Include profiles that list either Microsoft or GitHub, but only where the current role appears connected to GitHub.

Use this when acquired brands and parent-company labels overlap.

## How to interpret the results

The map is best used as a structured research asset, not an official employee directory.

A high count in a function may reflect:

* a genuinely large team;
* better profile visibility;
* more standardized titles;
* stronger coverage in the underlying profile index.

A low count may reflect:

* a genuinely small team;
* unusual or ambiguous job titles;
* employees using a subsidiary or product name;
* limited public-profile visibility;
* missing office locations;
* recent organizational changes;
* employees who have not updated their profiles.

Relative patterns are generally more useful than treating each count as exact.

For example, the map may reliably suggest that most Product employees sit in San Francisco and New York, even if it does not capture every employee in those offices.

## Common pitfalls

**Treating the map as an official organization chart** Public profiles rarely show complete reporting lines, internal team names, or every employee.

**Searching only the primary company name** Employees may list a former name, acquired brand, parent company, subsidiary, or regional legal entity.

**Using one broad query and stopping** A broad search can overrepresent common titles while missing smaller teams, unusual seniority labels, and regional employees.

**Reading mapped headcount as literal company headcount** The counts represent unique profiles found and included in the map. They are not official workforce numbers.

**Including consultants and advisors as employees** Profiles may mention a target company even where the person is providing external services. Evidence fields should make uncertain cases easier to review.

**Assuming all titles map cleanly to one function** Functions such as Solutions, Strategy, Analytics, and Operations often sit in different parts of different companies.

**Over-interpreting apparent gaps** A missing team may indicate limited profile coverage rather than a real organizational gap.

## Recommended workflow

1. Choose the target company and the function or functions to map.
2. Decide whether the map should be global or location-specific.
3. Add any known subsidiaries, acquisitions, former names, or regional entities.
4. Define adjacent teams that should be included or excluded.
5. Add an optional flag tied to the sourcing or research objective.
6. Generate the initial CSV and HTML overview.
7. Review the company aliases and office locations identified.
8. Check whether the function and seniority classifications match your expectations.
9. Inspect uncertain profiles and possible consultants or advisors.
10. Refine the scope and rerun the map if an important team, location, or alias appears underrepresented.
11. Use the CSV to filter and prioritize individuals, and the HTML overview to align stakeholders on the company’s structure.

The strongest talent maps are built iteratively. Use the initial output to identify missing aliases, unusual titles, and likely coverage gaps, then refine the scope until the map is useful for the sourcing or market-intelligence decision at hand.