Data Scientist

super.money; Bengaluru; On-site; INR 800,000 - 1,200,000; Full time

Job summary

super.money is seeking a Data Scientist to design, develop, and deploy machine learning solutions that solve business problems across the ML lifecycle, from data analysis and feature engineering to model productionisation.

The role emphasizes 2–4 years of experience in data science within fintech and risk domains. You will build scalable ML models, collaborate with engineering teams, and stay updated with evolving techniques to deliver business impact.

Qualifications

• 2–4 years of professional experience in data science.
• Strong foundation in ML concepts, statistical methods and model optimization.
• Hands-on mastery of Python (pandas, numpy, scikit-learn) and SQL databases.
• Advanced feature engineering, data preprocessing, and handling real-world datasets.
• Familiarity with model evaluation metrics and production deployment.

Responsibilities

• Develop and deploy machine learning models for classification, regression, and other predictive analytics use cases.
• Apply ML algorithms including Logistic Regression, Decision Trees, Random Forests, Gradient Boosting (XGBoost), ensemble methods, and neural networks where applicable.
• Perform feature engineering, data preprocessing, encoding, missing value treatment, and creation of meaningful business features.
• Analyze large datasets to identify patterns, generate insights, and build data-driven solutions.
• Evaluate model performance using metrics like AUC, Precision-Recall, F1, KS, Gini, PSI.
• Handle real-world ML challenges including data leakage, class imbalance, and model monitoring.
• Build scalable, production-ready ML solutions and collaborate with engineering for deployment.
• Document models with objectives, inputs, performance metrics, and limitations.
• Stay updated with ML advancements and apply relevant techniques to business problems.

Skills

Python

SQL

Pandas

NumPy

Scikit-learn

Feature engineering

Model deployment

Production-ready ML

ML lifecycle

Deep learning

XGBoost

Logistic Regression

Decision Trees

Random Forest

Gradient Boosting

Tools

Python

SQL

Job description

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.