Data Science Engineer

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.