Data Scientist

Role Overview

We are looking for a Data Scientist to design, develop, and deploy machine learning solutions that solve business problems. The role involves working across the ML lifecycle — from data analysis and feature engineering to model development, evaluation, and productionisation.

Experience Range:

2 – 4 Years of professional experience

Department / Domain:

Data Science & Analytics (Fintech / Risk / Fraud preferred)

Employment Type:

Full-Time / Permanent

Responsibilities

● Develop and deploy machine learning models for classification, regression, and other predictive analytics use cases.

● Apply strong understanding of machine learning algorithms including Logistic Regression, Decision Trees, Random Forests, Gradient Boosting models (XGBoost), Ensemble techniques, and Neural networks / deep learning approaches where applicable.

● Perform feature engineering, including data preprocessing, encoding, missing value treatment, feature transformations, and creation of meaningful business features.

● Analyse large datasets to identify patterns, generate insights, and build data-driven solutions. ● Evaluate model performance using appropriate metrics such as AUC, Precision-Recall, F1, KS, Gini, and PSI.

● Handle real-world ML challenges including class imbalance, model calibration, data leakage, and model performance monitoring.

● Build scalable and production-ready ML solutions and collaborate with engineering teams for deployment.

● Document models, including objectives, data inputs, performance metrics, and limitations. ● Stay updated with advancements in machine learning and apply relevant techniques to business problems.

Required Skills & Qualifications

● Experience building, improving, and independently scaling machine learning models. ● Strong foundation in machine learning concepts, statistical frameworks, and model optimization. ● Hands-on mastery of Python (pandas, numpy, scikit-learn) and SQL databases. ● Advanced feature engineering capabilities, data preprocessing, and experience handling real-world datasets (including class imbalances and complex workflows).

● Deep familiarity with structural model evaluation metrics, workflows, and execution strategies. ● Exposure to deep learning configurations, sequence models, or transfer learning approaches. ● Ability to build production-ready ML models, collaborate with engineering teams, and translate complex business problems into effective ML solutions.

Preferred Qualifications

● Experience working in fintech, lending, risk, fraud, or other data-intensive domains. ● Experience with model deployment, monitoring, and ML systems. ● Familiarity with research papers and emerging machine learning techniques.