Senior Data Scientist

2 weeks ago

Mysore, India CirrusLabs Full time

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, 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: 6-7 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.

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