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 jobs@wonderws.co / 9047477375 To Apply: https://careers.wonderws.com