Senior Data Scientist & Agentic AI

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.