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AI Research Scientist
AI Research ScientistCirrusLabs • Republic Of India, IN
AI Research Scientist

AI Research Scientist

CirrusLabs • Republic Of India, IN
24 days ago
Job description

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 : 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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