Data Scientist - Machine Learning Models

Description :

Your future duties and responsibilities :

- 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-cutting-edge methods into production.

Statistical and Mathematical Rigor :

- Strong grasp of descriptive and inferential statistics (hypothesis testing, A/B testing, regression, probability theory).

- Understanding of bias-variance trade-off, regularization, overfitting, and model validation
techniques.

Machine Learning & Deep Learning :

- Hands-on experience with a range of algorithms: decision trees, ensemble models, SVMs,
neural networks, clustering, and NLP techniques.

- Proficiency in deep learning architectures such as CNNs, RNNs, Transformers, and LSTMs.

Generative AI & LLMs :

- Conceptual and practical knowledge of Large Language Models (e.g., GPT, BERT, LLaMA), fine-
tuning, embeddings, and prompt engineering.

- Familiarity with generative modeling approaches (e.g., VAEs, GANs, diffusion models) is a
strong plus.

Programming & Problem Solving :

- Experience with libraries such as NumPy, pandas, matplotlib, scikit-learn, PyTorch, TensorFlow, and HuggingFace.

- Strong problem-solving skills with the ability to tackle coding challenges independently and efficiently.

Tooling & Deployment :

- Experience with cloud platforms (AWS, GCP, Azure), ML pipelines (MLflow, Airflow, Kubeflow), and containerization (Docker, Kubernetes).

- Version control (Git) and collaborative development practices.

Required Qualifications :

- Bachelors or masters degree in computer science, Statistics, Applied Mathematics, or a related field.

Statistical and Mathematical Rigor :

- Strong grasp of descriptive and inferential statistics (hypothesis testing, A/B testing, regression, probability theory).

- Understanding of bias-variance trade-off, regularization, overfitting, and model validation techniques.

Machine Learning & Deep Learning :

- Hands-on experience with a range of algorithms: decision trees, ensemble models, SVMs,
neural networks, clustering, and NLP techniques.

- Proficiency in deep learning architectures such as CNNs, RNNs, Transformers, and LSTMs.

Generative AI & LLMs :

- Conceptual and practical knowledge of Large Language Models (e.g., GPT, BERT, LLaMA), fine-
tuning, embeddings, and prompt engineering.

- Familiarity with generative modeling approaches (e.g., VAEs, GANs, diffusion models) is a
strong plus.

Programming & Problem Solving :

- Experience with libraries such as NumPy, pandas, matplotlib, scikit-learn, PyTorch, TensorFlow, and HuggingFace.

- Strong problem-solving skills with the ability to tackle coding challenges independently and
efficiently.

Tooling & Deployment :

- Version control (Git) and collaborative development practices.