Junior Data Scientist & Agentic AI

Experience : 3 years

Notice Period : Immediate joiner

Job Location : Bangalore (Hybrid)

Job Description:

Key Responsibilities

Core Modeling & Algorithmic Work

• Develop and optimize models for classification, regression, clustering, forecasting, and recommendation systems .
• Use a range of algorithms such as:
• Regression Models: Linear, Ridge, Lasso, ElasticNet, Quantile, Poisson, etc.
• Classification Models: Logistic Regression, Decision Trees, Random Forests, XGBoost, LightGBM, SVM, Neural Networks, etc.
• Unsupervised Learning: K-Means, DBSCAN, Hierarchical clustering, PCA, t-SNE, Autoencoders.
• Time Series & Forecasting: ARIMA, SARIMA, Prophet, LSTM, and hybrid models.
• Recommendation Systems: Collaborative filtering, Matrix factorization, Content-based and hybrid approaches.

Evaluation Metrics & Model Assessment

• Select appropriate evaluation metrics based on business goals and problem types:
• Classification: Accuracy, Precision, Recall, F1-score, ROC-AUC, PR-AUC, Log Loss, Cohen's Kappa, Matthews Correlation Coefficient.
• Regression: RMSE, MAE, R2, Adjusted R2, MAPE, SMAPE.
• Ranking/Recommenders: NDCG, , , Hit Rate.
• Clustering: Silhouette score, Davies-Bouldin Index, Calinski-Harabasz score.
• Forecasting: MSE, RMSE, MAPE, sMAPE, Theil's U statistic.
• Perform cross-validation , bootstrapping , and A/B testing for robust model validation.
• Monitor model drift, bias, and fairness across data slices.

Research & Experimentation

• Stay current with research trends in ML, DL, and applied AI (e.g., transformer models, self-supervised learning, and causal inference).
• Conduct experiments to improve baseline models using new architectures or ensemble approaches.
• Document hypotheses, results, and model interpretation clearly for cross-functional collaboration.

Required Skills & Qualifications

• Education: Master's or Bachelor's in Computer Science, Mathematics, Statistics, Data Science, or a related quantitative discipline.
• Experience: 3 years in core data science or applied ML, with end-to-end project ownership.
• Programming: Proficient in Python (pandas, NumPy, scikit-learn, statsmodels, XGBoost, LightGBM, TensorFlow/PyTorch).
• Data Handling: Strong in SQL and data wrangling with large-scale structured and unstructured datasets.
• Mathematics & Statistics: Excellent foundation in probability, linear algebra, optimization, and hypothesis testing.
• Model Evaluation: Proven expertise in selecting and interpreting metrics aligned to business goals.
• Visualization: Skilled in Matplotlib, Seaborn, Plotly , and storytelling with data-driven insights .
• Experience with MLOps , A/B testing , and data versioning tools (e.g., DVC, MLflow).

Nice to Have

• Knowledge of causal inference , Bayesian modeling , and Monte Carlo simulations .
• Familiarity with transformer-based models (BERT, GPT, etc.) for NLP tasks.
• Hands-on experience with graph analytics or network science .
• Experience mentoring junior data scientists and reviewing model design.
• Exposure to cloud ML stacks (AWS Sagemaker, GCP Vertex AI, or Azure ML Studio).

Soft Skills

• Strong analytical thinking and problem-solving orientation.
• Ability to balance scientific rigor with business pragmatism.
• Excellent communication both technical and non-technical audiences.
• Curious, self-driven, and comfortable working in fast-paced environments.