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Data Scientist

Actively Reviewing the Applications

The Versatile Club

India, Delhi, New Delhi, Hyderabad Full-Time

Posted 4 days ago • Apply by June 21, 2026

Job Description

Key Responsibilities

Develop and validate predictive models and AI algorithms under the guidance of senior

data science leads.

Perform data exploration, visualization, and feature selection for model development. 3. Implement classification, clustering, and forecasting models for government datasets. 4. Support AI/ML engineers in model training and evaluation activities.
Create comprehensive documentation for models and data pipelines.
Participate in performance testing and quality validation of AI outputs.
Ensure alignment of data science processes with NeGD's Responsible AI principles.

Technical Competencies

Machine Learning: Supervised/unsupervised learning, deep learning, NLP, computer vision, time series forecasting, and ensemble methods

Programming Languages: Python (scikit-learn, pandas, numpy), R for statistical analysis, SQL for data manipulation and analysis

ML Frameworks: TensorFlow, PyTorch, Keras, XGBoost, LightGBM, Hugging Face Transformers for model development

Data Processing: Advanced pandas, Apache Spark (PySpark), data wrangling, and large- scale data manipulation techniques

Statistical Analysis: Hypothesis testing, regression analysis, experimental design, and statistical modeling techniques

Visualization Tools: Matplotlib, Seaborn, Plotly, Tableau, Power BI for data exploration and results communication

Feature Engineering: Feature selection, dimensionality reduction (PCA, t-SNE), feature scaling, and data transformation techniques

Model Evaluation: Cross-validation, performance metrics, ROC curves, confusion matrices, and model interpretability (SHAP, LIME)

Database Technologies: SQL databases, NoSQL systems, and data warehouse querying for analytics and model training

Skills: feature engineering,data processing,ml,statistical analysis,programming languages,data,data visualization tools,model evaluation,ml frameworks,data science,machine learning