Job Recommender System for JSC
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Overview
In an increasingly competitive job market, Job Success Coaches (JSCs) at career development organizations face the challenge of efficiently matching learners with relevant job opportunities. These coaches need to navigate a complex array of job databases and learner profiles, which can be time-consuming and inefficient.
The Job Recommender System is based on a simple idea: streamline the job matching process by leveraging a sophisticated filtering system that considers various parameters like career track, job type, location, and learner's resume data.
What is Merit America?
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Problem
Job Success Coaches face significant challenges in efficiently matching each learner's profile with potential job openings. The manual process involves reviewing high volumes of positions, which is not only time-consuming but also prone to inaccuracies. Current systems lack a tailored experience for coaches, often leading to frustration and reduced effectiveness in identifying suitable job matches. The need for a more sophisticated, data-driven approach is evident to streamline this critical process.
Proposed Solutions
To address these challenges, we partnered with AdeptID, leveraging their advanced job matching technologies to revolutionize how job listings are matched to learner profiles. By integrating AdeptID's robust filtering algorithms, our system can automatically match job listings with learner resumes based on detailed criteria.
This partnership enables the implementation of dashboards for Job Success Coaches to visualize job market trends and track learner application statuses effectively. Additionally, we developed advanced search options tailored to the specific needs and qualifications of each learner, significantly enhancing the precision and efficiency of the job matching process.
User Personas
Defining user personas and mapping out user journeys gave deeper insights into the specific needs and behaviors of JSCs. This step was crucial for aligning the system’s design and functionality with real user requirements.
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Data-Driven Engineering for Enhanced Job Matching
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The screenshot from AdeptID’s documentation offers an overview of their Data Model, which is key to how their system matches candidates to jobs or occupations. The model includes core entities: Candidate (a job seeker’s profile with work experience, education, skills, etc.), Job (details of a job opening, such as title, required skills, and qualifications), Occupation (categories of jobs like "Electrical Engineers" or "Marketing Managers" for broader career transitions), and Match (assesses fit using a match score and skill gaps).
Additional supporting entities like Contact Info, Education, Employer, Industry, Location, and Skill enrich these profiles. This comprehensive model allows for precise and relevant job recommendations, which we integrated into our Job Recommender System to better match learners with suitable career opportunities.
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Job Placement Delivery Pilot
These mockups facilitate immediate job placement delivery by showing how job recommendations can be sent to learners via email and Slack, their most common communication channels. They also help clarify engineering requirements and explore integration possibilities with third-party services. The focus is on a short-term, effective solution to test the system's effectiveness and gather early user feedback.
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Iterating from Wireframes to Engineering-Ready Components
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Initial Wireframes and Design Discussions
The design journey began with the creation of wireframes for the crawl phase of the MVP. These initial wireframes laid the foundation for the Job Success Coach (JSC) flow, focusing on delivering “Merit-quality” job openings tailored to each career track they support. The primary user story for learners was: “As a learner, I want to get job recommendations so I can spend my time applying rather than sorting through and seeing if they fit my background.”
For JSCs, the goal was to simplify their workflow: “As a Job Success Coach, I want to get ‘generic’ Merit-quality job openings for each career track I am supporting so that I can spend my time helping my learners with their motivation and removing other barriers in their job search journey.”
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Leveraging Tailwind for Efficient Design and Development
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Engineering Ready
At Merit America, our design and engineering teams collaborated closely to leverage the power of Tailwind CSS for creating a highly efficient and scalable design system. By using Tailwind's utility-first approach, we were able to rapidly prototype and build new components that seamlessly integrated into our existing framework. This allowed us to maintain a consistent design language across the platform while speeding up the development process.
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Responsive Grid Specifications and Breakpoints
In our design system, grid specifications and breakpoints are essential for ensuring a responsive and user-friendly layout across all devices. Breakpoints are the defined screen sizes at which our content adapts to deliver the optimal viewing experience, whether on a desktop monitor, tablet, or smartphone. To achieve this, our grid system is designed to be flexible, adjusting column widths, gutters, and margins dynamically at each breakpoint.
This approach maintains consistency, readability, and usability, providing users with a seamless experience no matter what device they are using. By strategically defining breakpoints, we ensure our design remains fluid and accessible, allowing content to be consumed effortlessly across all screen sizes.
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Applying the Design System to Build the Job Success Coach
The robust design system, with its responsive grid specifications and well-defined breakpoints, played a crucial role in developing the Job Recommender system. Leveraging the consistent design language and reusable components, we efficiently created a user interface that was visually cohesive, functionally robust, and ready for engineering development.
By utilizing the grid system and breakpoints, the Job Recommender interface was optimized for usability across all devices, allowing Job Success Coaches to seamlessly manage job recommendations on any screen. The responsive design maintained a clear information hierarchy, simplifying navigation and enabling coaches to provide tailored support to learners. This streamlined approach ensured a high-quality, user-centric experience that could scale with the platform's growth..
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Breaking Down Components for Clearer Engineering Implementation
To facilitate a smoother development process and ensure alignment between design and engineering, I meticulously broke down the UI components of the Job Recommender system into smaller, manageable parts. This approach provided better visibility into each component's structure and functionality, enabling engineers to understand the design intent and build with precision.
By decomposing complex UI elements into atomic components—such as buttons, input fields, filters, and cards—I created a clear hierarchy and documentation for each element's state, behavior, and style variations. This breakdown allowed engineers to see exactly how components interact, their dependencies, and how they adapt across different breakpoints, reducing ambiguity and the potential for errors.
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Criteria-Based Search vs. Learner-Specific Search
Criteria-Based Search focuses on filtering job listings based on general criteria such as job type, location, salary range, and degree requirements. This type of search allows Job Success Coaches to quickly identify “Merit-quality” job openings across different career tracks without delving into individual learner details. It is particularly useful for generating a broad set of job recommendations that meet baseline requirements, making it easier for coaches to send out job lists that align with common learner needs.
On the other hand, Learner-Specific Search is tailored to individual learner profiles, taking into account factors like their resume, career history, skills, and preferences. This approach provides more personalized job recommendations that closely match a learner's background, experience, and specific career goals. While it requires more detailed input, it significantly improves the relevance of job matches, ensuring that the suggested jobs are more likely to align with what the learner is looking for and qualified to apply for.
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