Data Scientist || Celebal Technologies ||

Key Responsibilities:
†Generative AI Solutions: Develop innovative Generative AI solutions using machine learning and AI technologies, including building and fine-tuning models such as GANs, VAEs, and Transformers.
†Classical ML Models: Design and develop machine learning models (regression, decision trees, SVMs, random forests, gradient boosting, clustering, dimensionality reduction) to address complex business challenges.
†Deep Learning Systems: Train, fine-tune, and deploy deep learning models such as CNNs, RNNs, LSTMs, GANs, and Transformers to solve AI problems and optimize performance.
• NLP and LLM Optimization: Participate in Natural Language Processing activities, refining and optimizing prompts to improve outcomes for Large Language Models (LLMs), such as GPT, BERT, and T5.
• Data Management & Feature Engineering: Work with large datasets, perform data preprocessing, augmentation, and feature engineering to prepare data for machine learning and deep learning models.
• Model Evaluation & Monitoring: Fine-tune models through hyperparameter optimization (grid search, random search, Bayesian optimization) to improve performance metrics (accuracy, precision, recall, F1-score). Monitor model performance to address drift, overfitting, and bias.
• Code Review & Design Optimization: Participate in code and design reviews, ensuring quality and scalability in system architecture and development. Work closely with other engineers to review algorithms, validate models, and improve overall system efficiency.
• Collaboration & Research: Collaborate with cross-functional teams including data scientists, engineers, and product managers to integrate machine learning solutions into production. Stay up to date with the latest AI/ML trends and research, applying cutting-edge techniques to projects.

Qualifications:
• Educational Background: Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Data Science, or any related field.
• Experience in Machine Learning: Extensive experience in both classical machine learning techniques (e.g., regression, SVM, decision trees) and deep learning systems (e.g., neural networks, transformers). Experience with frameworks such as TensorFlow, PyTorch, or Keras.
• Natural Language Processing Expertise: Proven experience in NLP, especially with Large Language Models (LLMs) like GPT, BERT, or T5. Experience in prompt engineering, fine-tuning, and optimizing model outcomes is a strong plus.
• Programming Skills: Proficiency in Python and relevant libraries such as NumPy, Pandas, Scikit-learn, and natural language processing libraries (e.g., Hugging Face Transformers, NLTK, SpaCy).
• Mathematical & Statistical Knowledge: Strong understanding of statistical modeling, probability theory, and mathematical optimization techniques used in machine learning.
• Model Deployment & Automation: Experience with deploying machine learning models into production environments using platforms such as AWS SageMaker or Azure ML, GCP AI, or similar. Familiarity with MLOps practices is an advantage.
• Code Review & System Design: Experience in code review, design optimization, and ensuring quality in large-scale AI/ML systems. Understanding of distributed computing and parallel processing is a plus.

Soft Skills & Behavioural Qualifications:
• Must be a good team player and self-motivated to achieve positive results
• Must have excellent communication skills in English.
• Exhibits strong presentation skills with attention to detail.
• It’s essential to have a strong aptitude for learning new techniques.
• Takes ownership for responsibilities
• Demonstrates a high degree of reliability, integrity, and trustworthiness
• Ability to manage time, displays appropriate sense of urgency and meet/exceed all deadlines
• Ability to accurately process high volumes of work within established deadlines.