- 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.
Key Responsibilities:
- 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. , 0-1000), 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.
- Requirements - Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Economics, or a related field.
- 2-5 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.
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