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5 days ago
We are seeking a skilled professional to design and operationalize advanced forecasting solutions using machine learning and AI technologies.
The ideal candidate will have expertise in designing and deploying production-grade forecasting models, developing end-to-end machine learning pipelines for data ingestion, feature engineering, model training, and deployment, as well as building probabilistic models to quantify forecast uncertainty.
Key Responsibilities- Design, build, and deploy forecasting models
- Develop end-to-end machine learning pipelines for data ingestion, feature engineering, model training, and deployment
- Build probabilistic models to quantify forecast uncertainty
- Write clean, modular code following best practices
- Implement MLOps best practices including model versioning, monitoring, and CI/CD
- Work with distributed training frameworks for scalable model development
- Collaborate with cross-functional teams (engineering, product, strategy) to align solutions with business needs
- 3–6 years of experience in Data Science / ML Engineering
- Strong proficiency in Python and core libraries such as pandas, numpy, scikit-learn
- Hands-on experience with deep learning frameworks (preferably PyTorch)
- Experience with large-scale datasets and cloud environments
- Familiarity with MLOps practices (CI/CD, monitoring, retraining pipelines)
- Solid understanding of statistical modeling, regression, and time series
- Excellent problem-solving, communication, and collaboration skills
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