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
Contact us

Let us help you with your business challenges

Contact us to schedule a call or set up a meeting