L1 – Junior Machine Learner

The Role:

• As a Junior Machine Learner, you will work with the AI & Intelligence Team to build practical machine learning solutions for real business and operational problems.
• You will work across the ML lifecycle:
• Understand Problems → Prepare Data → Build Models → Validate → Deploy → Post-deployment Monitor →Explainability

What You Will Own - Your Responsibilities :

• Collect, clean, and prepare data for ML projects.
• Perform basic data analysis and identify patterns.
• Build and evaluate machine learning models.
• Perform feature engineering and model testing.
• Document model results, assumptions, and limitations.
• Support API-based deployment and model monitoring.
• Work with senior team members to improve model performance.
• Learn and apply ML best practices to real-world problems.

Non-Negotiable Metric :

• MODEL QUALITY + DELIVERY + OPERATIONAL IMPACT
• TARGET: ≥ 90% Model Validation Success | ≥ 90% On-Time Delivery
• Measured through data quality, model performance, validation results, delivery timelines, documentation, and measurable improvement in assigned operational outcomes.

The Fight: What You will Fight:

• Intelligence without impact.

A model is valuable only when it helps solve a real problem. You will fight poor data, weak validation, unnecessary complexity, and models that perform well in testing but fail to deliver practical value.

• Your goal is simple: build reliable intelligence that can be understood, used, and improved

WHO THRIVES HERE : Three Traits We Cannot Teach

• You measure impact, not accuracy.
• You govern your own work.
• You build to be understood.

THE WWS PERKS :

• Training in ML, RCM domain knowledge, and production deployment.
• Opportunity to work with real operational data and business problems.
• Exposure to the complete ML development lifecycle.
• Collaboration with AI, Technology, and Operations teams.

Qualifications:

• Bachelor's degree in Computer Science, AI, Data Science, Statistics, Mathematics, Engineering, or a related field.
• 1+ year of experience in Machine Learning, Data Science, Python, or a related technical area.
• Programming Languages & Tools: Python, Git.
• ML Libraries & Frameworks: Pandas, NumPy, Scikit-Learn, Matplotlib/Seaborn.
• Core Concepts: Data preprocessing, feature scaling, regression, classification, clustering, model evaluation metrics, and basic statistics.
• API Integration: Basic knowledge of REST APIs (e.g., FastAPI) for serving model predictions.
• Practical exposure to PyTorch or TensorFlow frameworks.
• Familiarity with the cloud service AWS.
• Active GitHub profile or Kaggle portfolio demonstrating real-world ML projects.
• Un derstanding of basic database management systems (MongoDB).
• Strong problem-solving and learning ability.

Contact us on [hidden] To Apply: [hidden]