Career Navigator Overview
Introduction
Career Navigator (CN) is a tool designed to assist employees in exploring potential career paths within their organization. These career paths are based on the employee’s skills, preferences, and goals.
Career paths aligned to your skills
Leverage AI to identify career paths that best match your unique skills and Career Interests, expanding beyond your current role. The AI takes into account these data inputs.
Mobile View: The mobile-optimized view is available for the Career Navigator application. For more information, read Career Navigator Mobile Application Guide.
Browse roles
Browse roles beyond your current skill set, functional areas using advanced search by skills, job families and more. Configure at Admin console > Career Navigator page.
Cascading Filters
When a user selects a value in any of the available filters (such as Skills, Business Function, or Business Unit), the remaining filters automatically update. This "cascading" effect restricts the available options in subsequent dropdowns to only those that match the criteria already defined by the initial selection.
View role details
Access comprehensive role details including descriptions, people currently in the role, open job positions, upskilling recommendations, and skill assessments. Configure at Admin console > Role Details page.
Product Functionality
Real-time Personalized Path Recommendations
Utilizing role titles, role skills & employee skills, work experiences, and career goals, CN dynamically generates personalized paths tailored to each individual's profile.
Explore roles
Search by skills, role title and filter by org entities like business function, job family etc.
Career Interests / Favorite Paths
Users can search for roles within the organization and mark them as career interests. Similarly, they can tag paths as favorite or dismiss ones that don't align with their goals.
Spotlight roles
They are important, quick-to-hire, or trending positions highlighted by the organization. They represent critical roles or current job openings your organisation wants to focus on. Showcasing these roles helps to generate interest among potential candidates.
For more information, refer Spotlight_roles
Role / Path Tags
Each role and path is tagged with relevant attributes such as domain switches or spotlight roles, offering users clear insights into unique opportunities and career trajectories. Users can filter the paths based on the tags as well.
Role Details
The Role Details page provides a comprehensive overview, including required skills, open job listings, individuals currently in the role, potential connections, achievable roles, and recommended courses to bridge skill gaps.
The Role Details page features improved UI to accommodate both Required and Relevant skills as defined in Talent Design (JIE).
- In-line Descriptions: Role descriptions are displayed in-line rather than in a separate side panel for a more cohesive reading experience.
Manually Added Next Roles
HR Admins can define specific "next roles" for a position that extend beyond standard AI recommendations. This allows organizations to guide employees toward specific business-critical paths.
Role Recommendations
The following logic applies to how roles are surfaced to employees:
- Guaranteed Visibility: At least one career path starting with a manually added role will always appear in the employee's recommendations.
- Ranking and Diversity: If multiple manual roles exist, at least one is shown, while others appear based on their ranking scores and diversity factors.
- AI Integration: This ensures a balanced mix of manual and automated suggestions.
- Fallback: If no manual roles are defined for a positionFallbacksystem continues to recommend AI-generated paths as usual.
How does AI work in Career Navigator Recommendations?
Eightfold’s Career Navigator suggests potential career paths within an organization, tailored to the employee's role skills and seniority configurations set by the organization. These path recommendations are personalized, offering employees a ranked list of potential career moves, as well as the option to explore paths toward roles of interest.
Inputs to the Model
Eightfold's recommendation model processes data from both organizational roles and employee profiles. Key role-related inputs include role titles, job levels, business functions, job codes, and skills. Employee profiles contribute data such as past role transitions within the organization, career interests, and current role skills.
How Does the Model Learn?
The model leverages these inputs to identify potential next roles based on seniority, typically allowing 0 or +1 level jumps. The top roles are selected using machine learning models that evaluate titles and skills. The model then constructs a graph (sequence) of roles and transitions by analyzing:
- Using Role & Org data :
- Title transitions* observed in employee profiles
- A title prediction model trained on a large dataset of public profiles
- Skill overlap between current and potential next roles (Skills present under Required by Role tab)
- Using Employee data** :
- Skill overlap between employee’s profile and potential next roles (Skills present under Other Skills)
* Employee title to Role mapping is done using the following logic - if some other employee (with an associated role) has the same current title as the current employee’s past experience OR If there is a role with the same title as the current employee’s past experience.
** Next roles generated using Employee data are unique to that employee.
How Are Paths Ranked?
Our path recommendation and search algorithm extracts candidate paths from this graph and ranks them relative to each for the best recommendations.
Path ranking considers:
- Seniority Gain: The increase in seniority over the course of the path.
- Transition Probability, determined by:
- Frequency of transitions seen across employee profiles.
- Prediction scores from the title prediction algorithm.
- Skill match between roles
- Similarity between role titles.
The ranking algorithm prioritizes paths that offer higher seniority gains, assuming similar transition probabilities, and paths with higher transition probabilities, assuming similar seniority gains. Typically, paths that involve sudden seniority jumps are ranked lower due to their lower probability of success.
Continuous Learning
The model continuously refines its recommendations through auto-calibration, learning from career progressions within the organization and any changes made to role skills.
Frequently Asked Questions
Topic: Manually Added Next Roles
Will this change remove AI-generated career paths?
No. AI-generated paths remain, but at least one manual role is guaranteed to be included.
Will this require UI changes?
No. Manual roles are surfaced naturally within the existing recommendations UI.
What if there are too many manually added roles?
At least one manual role path will be shown, with others appearing based on ranking scores and diversity factors.
What happens if a role is both manually added and AI-recommended?
The manually added role will take priority.
What if there are no manually added next roles?
The system will continue recommending AI-generated paths as usual.
Is the change in Role Design a result of this enhancement? How does this impact career path recommendation in Career Hub?
Yes, read Manually Added Next Roles for more details.
What is included in the Career Navigator release bundle?
The bundle includes: cascading filters (filter values update when any filter or search is selected); CN on mobile app v2; role details inline see more (full-screen display); required certification gap on role page; role consistency (search uses database-saved paths then backward-generated paths; consistent paths between Role details and Career Navigator); and manual include or exclude of next roles. Filters use existing config (career_navigator_config, advanced_role_search, filters_config).