Experience : 5 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, MAP@K, Recall@K, 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: 67 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

Senior Data Scientist & Agentic AI

Senior Data Scientist - Agentic AI & MLOps