Senior Data Scientist

Role & responsibilities

Experience: 6-8 years

Senior Data Scientist

end-to-end data science projects

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.