Data Scientist

We are building Saulikh, a platform transforming agri-trade with data science and AI. Our goal is to bring trust, transparency, and intelligence to commodity trading at scale.

Role Overview

We are seeking a Data Scientist to lead data-driven initiatives across trade, pricing, risk, and logistics. The role involves working with real-world datasets, building dashboards, applying ML models, and assisting in the development of prototypes for trust scoring, fraud detection, and forecasting.

• Collect, clean, and preprocess financial, transactional, and behavioral datasets from multiple sources.
• Design and implement trust/credit scoring algorithms that assess buyer/seller reliability and default risk.
• Engineer features from trade history, payment timeliness, disputes, cancellations, and external market data.
• Apply statistical models and machine learning techniques (logistic regression, random forests, gradient boosting, neural networks) to predict trustworthiness.
• Develop scoring frameworks normalized to a range (e.g., 01000), similar to CIBIL standards.
• Validate and back-test models against historical data to ensure accuracy, fairness, and stability.
• Build monitoring pipelines to track score performance, drift, and anomalies in real time.
• Work with business and product teams to ensure scores align with practical trade and risk management requirements.
• Document methodologies and communicate insights to technical and non-technical stakeholders.

• Bachelors or Masters degree in Computer Science, Statistics, Mathematics, Economics, or a related field.
• 25 years of experience as a Data Scientist, preferably in credit scoring, risk modeling, or fraud detection.
• Strong proficiency in Python (pandas, numpy, scikit-learn, statsmodels) or R.
• Experience with machine learning algorithms such as logistic regression, decision trees, random forests, gradient boosting (XGBoost/LightGBM), and neural networks.
• Strong SQL skills for handling large datasets; experience with data pipelines and API integrations.
• Domain knowledge of credit scoring principles, risk assessment, and regulatory considerations.
• Experience in model validation, back-testing, and ensuring fairness and interpretability.
• Excellent analytical and problem-solving skills with attention to detail.
• Strong communication skills for presenting findings to technical and business stakeholders.

The interested candidates may apply here.