Overview:
We are seeking a highly skilled Senior Data Scientist with a strong focus on applied machine learning and deep learning to develop and deploy impactful solutions at scale. This role is geared toward practitioners who thrive on solving real-world problems, not just designing models, but delivering measurable business value through robust, production-ready systems.
You will apply state-of-the-art algorithms and techniques to high-impact use cases, driving model performance, scalability, and accuracy.
Key Responsibilities:
- Design, train, and optimize machine learning and deep learning models (e.g., transformers, ensemble methods, deep reinforcement learning, probabilistic models, Vision, Speech detection, and LLMs).
- Leverage advanced mathematical and statistical techniques, including optimization, Bayesian inference, and causal ML, to enhance model performance and reliability.
- Drive end-to-end model development: from data exploration and feature engineering to tuning, validation, and real-world impact assessment.
- Work with large-scale datasets and apply distributed computing techniques for efficient training and inference workflows.
- Collaborate with engineering teams to integrate models into production environments, ensuring scalability and maintainability.
- Provide mentorship and technical guidance to junior data scientists in applied ML/DL practices and methodologies.
Required Qualifications:
- Minimum 4 years of hands-on experience in machine learning and deep learning, with a proven track record of building, tuning, and deploying models in production settings.
- Deep expertise in:
- Deep Learning (PyTorch or TensorFlow; CNNs, RNNs, transformers, attention mechanisms)
- Statistical Modeling (Bayesian methods, time-series forecasting, probabilistic ML)
- Mathematical Foundations (linear algebra, optimization, numerical methods)
- Proficiency in Python and/or R and a strong working knowledge of SQL.
- Demonstrated ability to deliver results with a business-oriented mindset—prioritizing practical impact over pure experimentation.
- Experience with large language models (LLMs), generative AI, or reinforcement learning.