Dhirendra Group

- 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.