Data Scientist

at Draup

Data Scientist

Draup

Company

Home

Data Scientist

at Draup

Posted by suchanya p

1 - 3 yrs

₹10L - ₹25L / yr

Bengaluru (Bangalore)

Skills

Machine Learning (ML)

Natural Language Processing (NLP)

Responsibilities

• Model Development and Optimization: Design, build, and deploy NLP models, including transformer models (e.g., BERT, GPT, T5) and other SOTA architectures, as well as traditional machine learning algorithms (e.g., SVMs, Logistic Regression) for specific applications.

• Data Processing and Feature Engineering: Develop robust pipelines for text preprocessing, feature extraction, and data augmentation for structured and unstructured data.

• Model Fine-Tuning and Transfer Learning:

Fine-tune large language models for specific applications, leveraging transfer learning techniques, domain adaptation, and a mix of deep learning and traditional ML models.

• Performance Optimization: Optimize model performance for scalability and latency, applying techniques such as quantization, ONNX formats etc.

• Research and Innovation: Stay updated with the latest research in NLP, Deep Learning, and Generative AI, applying innovative solutions and techniques (e.g., RAG applications, Prompt engineering, Self-supervised learning).

• Stakeholder Communication: Collaborate with stakeholders to gather requirements, conduct due diligence, and communicate project updates effectively, ensuring alignment between technical solutions and business goals.

• Evaluation and Testing: Establish metrics, benchmarks, and methodologies for model evaluation, including cross-validation, and error analysis, ensuring models meet accuracy, fairness, and reliability standards. • Deployment and Monitoring: Oversee the deployment of NLP models in production, ensuring seamless integration, model monitoring, and retraining processes.

Responsibilities

• Data Processing and Feature Engineering: Develop robust pipelines for text preprocessing, feature extraction, and data augmentation for structured and unstructured data.

• Model Fine-Tuning and Transfer Learning:

Fine-tune large language models for specific applications, leveraging transfer learning techniques, domain adaptation, and a mix of deep learning and traditional ML models.

• Performance Optimization: Optimize model performance for scalability and latency, applying techniques such as quantization, ONNX formats etc.

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About Draup

Founded :

2017

Type :

Services

Size

Stage :

Profitable

About

Draup is an enterprise decision-making platform for global CXO leaders in sales and talent domains. Draup combines machine learning with human curation to help organizations make data-driven strategic decisions. The platform is powered by machine-generated models, which are augmented by a team of analysts adding their learning-based insights to provide a 360-degree transactable view of their sales and talent ecosystem.

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