Description :

Experience : 6-8 years

Location : Bengaluru

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.

You will work on end-to-end data science projects from hypothesis formulation, feature engineering, and model selection to rigorous evaluation and deployment.

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.

- 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 :

- 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.