Data Science

Essential Technical Skills

The candidate shall possess the following technical competencies, which are considered essential for the effective discharge of the responsibilities attached to the position.

A. Programming and Data Handling

1. The candidate should have strong proficiency in Python, with particular expertise in the pandas, NumPy and scikit-learn libraries.
2. The candidate should possess strong SQL skills, including the use of joins, aggregations, subqueries, common table expressions (CTEs) and window functions.
3. The candidate should be able to process large datasets efficiently and to identify and resolve issues arising in data processing.
4. The candidate should have practical experience in data cleaning, transformation and aggregation, and in the preparation of datasets for modelling.

B. Statistics and Machine Learning

1. The candidate should have a sound understanding of probability, statistics, sampling, hypothesis testing, classification and regression.
2. The candidate should have practical knowledge of logistic regression, decision trees, random forests and gradient boosting techniques.
3. The candidate should have hands-on experience with at least one boosting framework, namely XGBoost, LightGBM or CatBoost.
4. The candidate should have experience in feature engineering, feature selection, treatment of missing values and handling of imbalanced classification problems.
5. The candidate should have a clear understanding of cross-validation, hyperparameter tuning, data leakage and overfitting, and of the measures required to address them.
6. The candidate should be able to select and interpret appropriate evaluation metrics having regard to business objectives, event rates and operational requirements.

   Role & responsibilities

Preferred candidate profile

• Minimum experience 3 Years
• Educational qualification: BTech/ BE / MCA (Computer Science and Information Technology)