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Mid-level metro role at a strong global brand with a common title increases competition.
Specialized deep-learning and production ML skills are moderately transferable across industries.
Explicit 4–6 years plus deep ML, DL, PyTorch, Spark, and cloud requirements make filters strict.
Translate ambiguous business problems into mathematical objectives and optimization targets.
Develop production-grade, modular code for scalable mathematical models and data pipelines processing massive datasets.
Build, train, fine-tune complex neural networks across text, audio, and visual modalities with deployment to cloud environments.
4 to 6 years of industry experience as Data Scientist or Machine Learning Engineer with complex multimodal projects.
Advanced proficiency in Python with OOP, scikit-learn, PyTorch or TensorFlow, Apache Spark (PySpark), Hadoop ecosystem, and cloud platforms (AWS, Azure, or GCP).
Bachelor’s, Master’s, or Ph.D. in Mathematics, Statistics, Econometrics, Computer Science, Physics, or Operations Research.
Strong foundational mathematics (linear algebra, multivariable calculus, probability), machine learning, NLP, and computer vision expertise as detailed.
Experienced designing and deploying scalable ML applications on cloud infrastructure for industrial or automation domains.
Proficient in advanced deep learning architectures including Transformers for NLP and vision, and productionizing large-scale data solutions.
Strong quantitative background with demonstrated ability to translate ambiguous problems into mathematically rigorous solutions and clean, maintainable code.