Transform Your Idea

Recommender System for Staffing Process

Client: Global staffing solutions provide
QA
Expertise:
Tech Stack:
DevOps
UX/UI
PM
AI/ML
Frontend
GitHub
Numpy
Scikit-Learn
PyTorch
Pandas
Docker Compose
Kafka
PostgreSQL
AWS (S3, Redshift, Kinesis, Sagemaker, Studio, Pipelines, Experiments)
Docker
MLFlow
Optuna
Business Analyst
Backend
The client is a global staffing solutions provider aiming to improve efficiency and decision-making in their talent acquisition and allocation processes. Their goal is to leverage data-driven tools to streamline operations and match candidates to roles with higher precision.
About the Client:
Our task was to develop a custom recommender system to optimize staffing processes. The system needed to integrate specific business rules and provide transparency in decision-making through explainable AI. It aimed to rank and recommend candidates efficiently while adapting to ongoing changes in business processes.
About the Project

Challenges & Objectives

Challenges:
  1. Complex Business Rules: Incorporating unique rules and restrictions into the model while maintaining flexibility.
  2. Data Drift: Adapting to frequent changes in candidate profiles and job requirements.
  3. Explainability: Ensuring the system provided understandable reasons for each recommendation.
Objectives:
  1. Build a scalable recommendation engine that ranks candidates effectively.
  2. Integrate an explainability layer for transparency and user trust.
  3. Develop mechanisms to detect and handle data drift dynamically.
Implementation
  • 01
    Data Flow and Preprocessing:
    • Designed data pipelines using AWS S3, Redshift, and Kinesis for real-time data ingestion.
    • Processed data with Pandas, Numpy, and Scikit-Learn to prepare tabular datasets for modeling.
  • 02
    Model Development:
    • Created a ranking algorithm centered on a Deep Neural Network (DNN) with a custom multi-headed loss function.
    • Incorporated penalties for feedback from various environmental factors to improve recommendation accuracy.
    • Designed explainability layers to provide insights into each recommendation using MLFlow.
  • 03
    Data Drift Management:
    • Implemented detection and handling mechanisms to identify and adapt to data changes.
    • Utilized tools like Optuna for hyperparameter optimization and enhanced model resilience.
  • 04
    System Deployment:
    • Containerized the solution using Docker and deployed it on AWS Sagemaker for scalability.
    • Integrated Redis for real-time model inference and Kafka for communication between services.

Outcomes & Business Impact

Results:
  1. Improved Matching Accuracy: Enhanced precision in candidate-role matching through advanced ranking algorithms.
  2. Scalable Deployment: Enabled real-time recommendations for large-scale staffing operations.
  3. Enhanced Trust: The explainability layer increased user confidence in the system’s decisions.
  4. Future-Proof Design: Addressed data drift dynamically, ensuring long-term usability.
Business Impact:
  1. +40% Efficiency Gains: Reduced time-to-fill for open positions by streamlining candidate selection.
  2. +25% Manager Productivity: Automated repetitive tasks, allowing managers to focus on strategic decisions.
  3. Higher User Adoption: Transparent recommendations fostered trust among HR teams and hiring managers.
  4. Scalability: Positioned the client as a leader in AI-driven staffing solutions.
  • +30% Accuracy in Role Matching: Precise candidate recommendations improved hiring outcomes.
  • +40% Faster Processing: Real-time inference accelerated decision-making.
  • +20% Cost Savings: Automation reduced operational expenses.
  • Resilience: Data drift detection ensured model adaptability over time.
Results & ROI
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