Data Scientist - Banking

Core Responsibilities

Design, build and optimize predictive models and machine learning algorithms using structured and semi-structured data.

Perform data pre-processing, feature engineering and model selection independently.

Build and maintain automated model pipelines including training, validation, scoring and monitoring.

Implement model drift detection, retraining logic and performance diagnostics.

Conduct code-based model explainability (e.g., SHAP, LIME) and support documentation for governance review.

Expertise Required

Advanced proficiency in Python (Pandas, NumPy, Scikit-learn, XGBoost, LightGBM)

Strong command of SQL and handling large datasets (via warehouse or lake)

Experience deploying models using MLflow, Airflow, Docker or similar tools

Familiarity with model performance metrics (ROC AUC, F1, lift / gain, etc.)

Hands‑on experience in training and evaluating models for binary classification, multi‑class regression or time series

Exposure to deep learning (PyTorch or TensorFlow) for advanced use cases

Working knowledge of embeddings, vector stores or text‑based models

Git‑based versioning and reproducible ML workflow setup

Joining time frame 2 weeks (maximum 1 month)

Remote Work No

Employment Type Full‑time

Key Skills

Laboratory Experience

Immunoassays

Machine Learning

Biochemistry

Assays

Research Experience

Spectroscopy

Research & Development

cGMP

Cell Culture

Molecular Biology

Data Analysis Skills

Experience : years

Vacancy : 1

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