Senior Data Scientist
Description
- Partner with business stakeholders to identify and solve high-impact business problems using data and AI.
- Build predictive, prescriptive, and optimization models to improve revenue, profitability, customer satisfaction, quality, and operational efficiency.
- Develop end-to-end ML solutions from problem formulation, feature engineering, model development, deployment, monitoring, and business adoption.
- Present insights and recommendations to senior leadership and functional teams.
- Mentor junior data scientists and contribute to AI/ML strategy and best practices.
- Classification: Logistic Regression, Decision Trees, Random Forest, XGBoost, LightGBM, CatBoost, SVM, Ensemble Methods.
- Regression: Linear/Regularized Regression, Tree-based Regression Models, Gradient Boosting Models.
- Clustering & Segmentation: K-Means, Hierarchical Clustering, DBSCAN.
- Statistical Analysis, Hypothesis Testing, A/B Testing, Forecast Accuracy Measurement, Causal Inference.
- ARIMA, SARIMA, Prophet, Exponential Smoothing, VAR.
- ML-based Forecasting using XGBoost, Random Forest, LightGBM.
- Deep Learning Forecasting using LSTM, GRU, Temporal Fusion Transformer (TFT), Transformer-based models.
- Applications: Demand Forecasting, Sales Forecasting, Inventory Planning, Spare Parts Forecasting, Production Forecasting.
- Neural Networks (ANN, DNN)
- NLP: BERT, LLMs, Generative AI, RAG Frameworks
- Computer Vision: CNN, ResNet, YOLO
- Experience with GenAI, Prompt Engineering, AI Agents is an added advantage.
- Python, SQL, PySpark
- Pandas, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM
- Databricks, Spark, AWS/GCP
- MLflow, Docker, Kubernetes, MLOps
- Power BI/Tableau for visualization and storytelling
This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Skills
- A/B Testing
- Agentic AI
- AI
- Analytics
- AWS
- Causal Inference
- Computer Vision
- Data Science
- Databricks
- Deep Learning
- Docker
- Feature Engineering
- GCP
- Generative AI
- Kubernetes
- LLM
- Machine Learning
- MLflow
- MLOps
- Neural Networks
- NLP
- pandas
- Power BI
- Prompt Engineering
- PySpark
- Python
- PyTorch
- scikit-learn
- Spark
- SQL
- Statistics
- Tableau
- TensorFlow
- Time Series
- XGBoost
- YOLO