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

Data Scientist

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.g., 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
.
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.

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