Data Scientist- LLM/GenAI

Huey Tech Pvt. Ltd.; Dadri; On-site; INR 900,000 - 1,800,000; Full time

Job summary

Huey Tech Pvt. Ltd. in India seeks a data science professional to perform data preparation, feature engineering, and exploratory analyses across transactional and behavioral datasets.

You will build predictive models for risk scoring and propensity, define timing windows, and guard against leakage. The role emphasizes rigorous evaluation using AUC/KS/Gini, model explainability with SHAP, and collaboration with engineering for production features and monitoring.

Qualifications

• Strong Python and SQL skills with experience on large datasets.
• Hands-on experience with logistic regression and tree-based models such as XGBoost, LightGBM, or CatBoost.
• Solid grounding in statistics and experimentation, including sampling bias, class imbalance, and leakage handling.
• Experience evaluating models using AUC, KS, Gini, precision/recall, calibration, and stability measures like PSI.
• Familiarity with explainability techniques such as SHAP and the ability to explain model behaviour to non-technical stakeholders.
• Experience contributing to model deployment and monitoring in production environments.
• Bachelor's or master's degree in statistics, mathematics, computer science, engineering, economics, or related quantitative field.

Responsibilities

• Perform data preparation, feature engineering, and exploratory analysis on transactional and behavioral data.
• Develop predictive models for risk scoring, underwriting, propensity, and forecasting.
• Define target variables, observation windows, and avoid data leakage.
• Validate models using out-of-time testing and assess discrimination, calibration, and stability.
• Translate model outputs into decision rules, thresholds, and segments.
• Assist deployment and monitoring; suggest recalibration or retraining as needed.
• Collaborate with engineering to align training and production features.
• Maintain documentation, reproducibility, and explainability for governance.
• Contribute to AI/LLM initiatives through design, evaluation, or integration.

Skills

Python

SQL

Logistic regression

Tree-based models (XGBoost, LightGBM,

CatBoost

Statistics & experimentation

SHAP explainability

Production deployment

Model evaluation

Education

Bachelor's or Master's degree in statistics, mathematics, computer science, engineering, economics, or related quantitative field

Tools

SHAP

Job description

Key Responsibilities

• Perform data preparation, feature engineering, and exploratory analysis on transactional, behavioural, third-party, and alternative data.
• Develop predictive models for risk scoring, underwriting, propensity, early-warning signals, forecasting, and workflow prioritisation.
• Define target variables, observation windows, and performance windows carefully, accounting for time lags, maturity, and data leakage.
• Validate models using out-of-time testing and assess performance on discrimination, calibration, stability, and business impact.
• Help turn model outputs into practical decision rules, thresholds, segments, and action strategies.
• Support model deployment, monitoring for drift and performance, and recommend recalibration or retraining when needed.
• Partner with engineering to keep training and production features consistent and to support reliable pipelines.
• Maintain clear documentation, reproducibility, and explainability so models can support governance and review.
• Contribute to AI/LLM initiatives (e.g., virtual CFO or agent-style workflows) through feature design, evaluation, or integration as needed.

Required Skills and Experience

• Strong proficiency in Python and SQL, with experience working on large datasets.
• Practical experience with logistic regression and tree-based models (XGBoost, LightGBM, CatBoost, or similar), and knowing when simpler models are preferable.
• Solid grounding in statistics and experimentation, including sampling and selection bias, class imbalance, and careful treatment of leakage.
• Experience evaluating models using AUC, KS, Gini, precision/recall, calibration, and stability measures such as PSI.
• Familiarity with explainability techniques such as SHAP and ability to explain model behaviour to non-technical stakeholders.
• Experience contributing to model deployment and monitoring in production environments.
• Bachelors or masters degree in statistics, mathematics, computer science, engineering, economics, or a related quantitative field.

Nice to Have

• Exposure to deploying or evaluating AI/LLM inference workflows (latency, cost, reliability, output quality).
• Familiarity with LLM tool/function calling, external API integration, or multi-step agent-style workflows.
• Understanding of supervised fine-tuning, instruction tuning, or preference optimisation approaches.

Experience putting evaluation, guardrails, observability, or human review in place for sensitive AI