Design, build, and validate machine learning and deep learning models, ensuring robustness, scalability, and explainability.
- Apply strong statistical foundations to analyze large datasets and derive actionable insights.
- Lead the development and evaluation of models using modern ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
- Drive the adoption of Generative AI and LLM-based solutions, ensuring model alignment, prompt engineering, and ethical AI practices.
- Collaborate with data engineers, product teams, and business stakeholders to transform business problems into technical solutions.
- Contribute to code reviews, model documentation, and mentorship of junior data scientists.
Stay abreast of the latest research and translate cutting-edge methods into production.
- Statistical and Mathematical Rigor
o Strong grasp of descriptive and inferential statistics (hypothesis testing, A/B testing, regression, probability theory).
o Understanding of bias-variance trade-off, regularization, overfitting, and model validation techniques.
- Machine Learning & Deep Learning
o Hands-on experience with a range of algorithms: decision trees, ensemble models, SVMs, neural networks, clustering, and NLP techniques.
o Proficiency in deep learning architectures such as CNNs, RNNs, Transformers, and LSTMs.
o Conceptual and practical knowledge of Large Language Models (e.g., GPT, BERT, LLaMA), fine-tuning, embeddings, and prompt engineering.
o Familiarity with generative modeling approaches (e.g., VAEs, GANs, diffusion models) is a strong plus.
- Programming & Problem Solving
o Advanced proficiency in Python with the ability to write clean, modular, and testable code.
o Experience with libraries such as NumPy, pandas, matplotlib, scikit-learn, PyTorch, TensorFlow, and HuggingFace.
o Strong problem-solving skills with the ability to tackle coding challenges independently and efficiently.
o Experience with cloud platforms (AWS, GCP, Azure), ML pipelines (MLflow, Airflow, Kubeflow), and containerization (Docker, Kubernetes).
o Version control (Git) and collaborative development practices.Required qualifications to be successful in this role
- Bachelor’s or master’s degree in computer science, Statistics, Applied Mathematics, or a related field.
- Statistical and Mathematical Rigor
o Strong grasp of descriptive and inferential statistics (hypothesis testing, A/B testing, regression, probability theory).
o Understanding of bias-variance trade-off, regularization, overfitting, and model validation techniques.
- Machine Learning & Deep Learning
o Hands-on experience with a range of algorithms: decision trees, ensemble models, SVMs, neural networks, clustering, and NLP techniques.
o Proficiency in deep learning architectures such as CNNs, RNNs, Transformers, and LSTMs.
o Conceptual and practical knowledge of Large Language Models (e.g., GPT, BERT, LLaMA), fine-tuning, embeddings, and prompt engineering.
o Familiarity with generative modeling approaches (e.g., VAEs, GANs, diffusion models) is a strong plus.
- Programming & Problem Solving
o Advanced proficiency in Python with the ability to write clean, modular, and testable code.
o Experience with libraries such as NumPy, pandas, matplotlib, scikit-learn, PyTorch, TensorFlow, and HuggingFace.
o Strong problem-solving skills with the ability to tackle coding challenges independently and efficiently.
