Job description
<p><p><b>Description : </b><br/><br/>Experience : 6-8 years<br/><br/>Location : Bengaluru<br/><br/>We are looking for a Senior Data Scientist with 6-8 years extensive experience in designing, building, and evaluating machine learning models for diverse business use cases.
The role requires strong fundamentals in mathematics, statistics, and computer science with a focus on model interpretability, evaluation metrics, and production-readiness.<br/><br/>You will work on end-to-end data science projects from hypothesis formulation, feature engineering, and model selection to rigorous evaluation and deployment.<br/><br/><b>Key Responsibilities : </b><br/><br/><b>Core Modeling & Algorithmic Work : </b><br/><br/>- Develop and optimize models for classification, regression, clustering, forecasting, and recommendation systems.<br/><br/>Use a range of algorithms such as : <br/><br/>- Regression Models : Linear, Ridge, Lasso, ElasticNet, Quantile, Poisson, etc.<br/><br/>- Classification Models : Logistic Regression, Decision Trees, Random Forests, XGBoost, LightGBM, SVM, Neural Networks, etc.<br/><br/>- Unsupervised Learning : K-Means, DBSCAN, Hierarchical clustering, PCA, t-SNE, Autoencoders.<br/><br/>- Time Series & Forecasting : ARIMA, SARIMA, Prophet, LSTM, and hybrid models.<br/><br/>- Recommendation Systems : Collaborative filtering, Matrix factorization, Content-based and hybrid approaches.<br/><br/><b>Evaluation Metrics & Model Assessment : </b><br/><br/>- Select appropriate evaluation metrics based on business goals and problem types : <br/><br/>- Classification : Accuracy, Precision, Recall, F1-score, ROC-AUC, PR-AUC, Log Loss, Cohen's Kappa, Matthews Correlation Coefficient.<br/><br/>- Regression : RMSE, MAE, R2, Adjusted R2, MAPE, SMAPE.<br/><br/></p><p>- Forecasting : MSE, RMSE, MAPE, sMAPE, Theil's U statistic.<br/><br/>- Perform cross-validation, bootstrapping, and A/B testing for robust model validation.<br/><br/>- Monitor model drift, bias, and fairness across data slices.<br/><br/><b>Research & Experimentation : </b><br/><br/>- Stay current with research trends in ML, DL, and applied AI (e.g., transformer models, self-supervised learning, and causal inference).<br/><br/>- Conduct experiments to improve baseline models using new architectures or ensemble approaches.<br/><br/>- Document hypotheses, results, and model interpretation clearly for cross-functional collaboration.<br/><br/><b>Required Skills & Qualifications : </b><br/><br/>- Education : Master's or Bachelor's in Computer Science, Mathematics, Statistics, Data Science, or a related quantitative discipline.<br/><br/>- Experience : 67 years in core data science or applied ML, with end-to-end project ownership.<br/><br/>- Programming : Proficient in Python (pandas, NumPy, scikit-learn, statsmodels, XGBoost, LightGBM, TensorFlow/PyTorch).<br/><br/>- Data Handling : Strong in SQL and data wrangling with large-scale structured and unstructured datasets.<br/><br/>- Mathematics & Statistics : Excellent foundation in probability, linear algebra, optimization, and hypothesis testing.<br/><br/>- Model Evaluation : Proven expertise in selecting and interpreting metrics aligned to business goals.<br/><br/>- Visualization : Skilled in Matplotlib, Seaborn, Plotly, and storytelling with data-driven insights.<br/><br/>- Experience with MLOps, A/B testing, and data versioning tools (e.g., DVC, MLflow).<br/><br/><b>Nice to Have : </b><br/><br/>- Knowledge of causal inference, Bayesian modeling, and Monte Carlo simulations.<br/><br/>- Familiarity with transformer-based models (BERT, GPT, etc.) for NLP tasks.<br/><br/>- Hands-on experience with graph analytics or network science.<br/><br/>- Experience mentoring junior data scientists and reviewing model design.<br/><br/>- Exposure to cloud ML stacks (AWS Sagemaker, GCP Vertex AI, or Azure ML Studio).<br/><br/><b>Soft Skills : </b><br/><br/>- Strong analytical thinking and problem-solving orientation.<br/><br/>- Ability to balance scientific rigor with business pragmatism.<br/><br/>- Excellent communication - both technical and non-technical audiences.<br/><br/>- Curious, self-driven, and comfortable working in fast-paced environments.</p><br/></p> (ref:hirist.tech)
Required Skill Profession
Mathematical Science Occupations