Data Scientist

Experience:

• 5–8 years in AI/ML, with a strong background in working with diverse data modalities, including text, images, and audio.

Core Expertise:

• Machine Learning Algorithms: Proficient in designing, training, and optimizing machine-learning models, including deep learning architectures like CNNs, RNNs, and Transformers.
• NLP Expertise: Knowledgeable in advanced NLP techniques, including text representation (e.g., embedding, tokenization), and working with language models such as BERT, GPT, or similar frameworks.
• Image and Audio Data: Experience in preprocessing, feature extraction, and modeling for image and sound datasets, including familiarity with computer vision techniques and audio signal processing.

Technical Proficiency:

• Programming Languages: Strong proficiency in Python; knowledge of R, Java, or Node.js is a plus.
• Frameworks: Hands-on experience with ML/DL libraries like TensorFlow, PyTorch, Keras, and Scikit-learn.
• Data Handling: Expertise in data preprocessing and analysis using libraries like Pandas and NumPy, with a focus on large-scale datasets.API Development: Proficiency in REST API development and integration, along with NoSQL and RDBMS database design.

• Infrastructure and Optimization:

• Familiar with high-performance computing (GPU acceleration, distributed training) and cloud-based ML pipelines on AWS (e.g., SageMaker)
• Understanding of optimization techniques for both algorithms and hardware to enhance training and inference efficiency.

Version Control and Collaboration:

• Proficient in version control systems (e.g., Git) and comfortable collaborating in cross-functional teams.

Soft Skills:

• Strong analytical and problem-solving abilities.
• Effective communication skills to bridge technical and non-technical stakeholders.