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Draup

Compare

3.4

based on 92 Reviews

12 Draup Jobs

Senior Data Scientist Draup

Draup

3.4

based on 92 Reviews

5-7 years

Bangalore / Bengaluru

1 vacancy

Senior Data Scientist Draup

Draup

posted 1mon ago

Job Role Insights

Flexible timing

Key skills for the job

Python Machine Learning Google Analytics Natural Language Processing Deep Learning CCTV Monitoring

+ 2 more

Job Description

Job Summary
We are seeking a highly skilled and experienced Senior Data Scientist to join our team. In this
role, you will leverage advanced Natural Language Processing (NLP), Deep Learning, and
traditional Machine Learning (ML) techniques to build and optimize our NLP-driven products
and services. You will work closely with cross-functional teams to develop scalable, highperformance NLP models that solve complex language and data-related challenges. If you are
passionate about advancing language understanding and have hands-on experience with the
latest NLP frameworks, models, and techniques, we would love to speak with you!

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.

Requirements
Minimum Qualifications

Education: Bachelor s degree in computer science, or a related field.

Experience: Minimum of 2.5 to 5 years of experience in NLP, Deep Learning, and ML,
with a proven track record of developing and optimizing both LLM and traditional
machine learning NLP models.

Technical Skills

Advanced NLP Techniques: Proficiency in transformer models (e.g., BERT, RoBERTa,
GPT, T5), and experience with techniques like entity recognition (NER), text
classification, summarization, question answering, and language generation.

Programming: Strong programming skills in Python, with experience in NLP libraries
such as Hugging Face Transformers, spaCy, NLTK, and Gensim.

ML and DL Frameworks: Proficiency in ML and Deep Learning frameworks such as
TensorFlow, PyTorch, and Scikit-learn.

Traditional ML Techniques: Familiarity with traditional ML models like SVMs, Logistic
Regression, Decision Trees, and KNN etc

Evaluation Metrics: Familiarity with NLP and ML evaluation metrics (e.g., BLEU,
ROUGE, F1, accuracy, precision, recall) and experience designing experiments and
tests.

Soft Skills

Communication Skills: Excellent verbal and written communication skills, capable of
explaining technical concepts to both technical and non-technical stakeholders.