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.

Generative AI & LLMs 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.

Tooling & Deployment 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.

Generative AI & LLMs 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.

Tooling & Deployment

Skills

• English
• Python
• Teradata
• ETL