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Data Scientist

Maharashtra, India (Remote)

Data Scientist

3 Months ago • All levels • Data Analyst • Artificial Intelligence • Undisclosed

About the job

15 skills required for this role

Add these skills to join the top 1% applicants for this job

game-texts

tensorflow

prototyping

transfer-learning

data-science

cross-functional

ci-cd

kubernetes

pandas

azure

aws

python

docker

pytorch

model-serving

Job Description

This remote Data Scientist role focuses on developing and applying advanced Large Language Models (LLMs) to enhance AI-driven healthcare solutions. Must-have skills include expertise in LLMs, NLP, Python, and experience fine-tuning pre-trained LLMs for specific tasks. Ideal candidates will possess strong problem-solving skills, a passion for healthcare data, and a collaborative mindset.

Must have:

LLMs expertise
NLP proficiency
Python experience
Fine-tuning LLMs

Good to have:

Commercial LLMs
Transfer learning
RAG experience
Cloud platforms

Perks:

Remote working
Flexible hours

About the job

This is a remote position.

We are seeking a highly skilled Data Scientist with expertise in Large Language Models (LLMs) and Natural Language Processing (NLP). In this role, you will lead the development and application of advanced LLMs to enhance our AI-driven solutions, drive innovation in language understanding, and enable meaningful insights from vast datasets. This position requires deep knowledge in data science, machine learning (ML), and the ability to fine-tune and deploy LLMs to solve real-world problems.

You will be working with cross-functional teams of healthcare professionals, data scientists, engineers, and business stakeholders to define and deliver cutting-edge solutions that transform healthcare delivery. If you are a highly motivated and results-oriented individual with a passion for leveraging healthcare data to drive insights and improve patient outcomes, we would love to hear from you.

Essential Duties And Responsibilities:

Lead the design, development, and deployment of LLM-based solutions to address complex business challenges.

Conduct advanced research and experiment with commercial LLM solutions (OpenAI GPT, Gemma, Llama), and leverage their APIs for rapid prototyping.

Fine-tune pre-trained LLMs (e.g., GPT, BERT, T5) to suit specific use cases, ensuring high performance on natural language tasks.

Utilize transfer learning techniques to adapt LLMs for domain-specific tasks.

Develop and implement strategies for data augmentation and synthesis to improve model generalization.

Collaborate with cross-functional teams (engineering, product management, and operations) to integrate LLMs into various applications, such as chatbots, knowledge retrieval systems, and data analytics platforms.

Conduct advanced research in the field of LLMs to stay updated with cutting-edge methodologies and apply them to real-world problems.

Build pipelines for preprocessing, training, and deploying models, ensuring scalability and reliability.

Evaluate and optimize models using metrics such as accuracy, F1 score, perplexity, and computational efficiency.

Create data pipelines for large-scale text data ingestion, cleaning, and annotation.

Perform A/B testing and rigorous validation of LLM-based models, providing insights on model efficacy.

Mentor junior data scientists and engineers on NLP techniques and best practices.

Document model development and analysis processes for transparency and reproducibility.

Requirements

Qualifications:

Bachelor's degree in Data Science, Computer Science, Machine Learning, or a related field.

Master’s degree is preferred

Skill Sets:

Hands-on experience with popular LLM architectures (GPT, BERT, RoBERTa, T5, etc.) and fine-tuning them for specific tasks.

Familiarity with commercial LLM solutions such as GPT, Gemma, Gemini, Llama.

Proficiency in programming languages such as Python and frameworks like PyTorch or TensorFlow.

Expertise in building and deploying machine learning models, including experience with model serving frameworks like TensorFlow Serving, MLflow, or similar.

Experience in fine-tuning pre-trained LLMs and applying transfer learning to adapt models for domain-specific tasks.

Familiarity with Retrieval-Augmented Generation (RAG) for improving model responses by integrating external knowledge bases or search engines.

Experience using Jupyter Notebooks for experimentation, prototyping, and data analysis.

Experience with cloud platforms (AWS, GCP, or Azure) for scalable model deployment.

Strong knowledge of data preprocessing, feature engineering, and text vectorization methods (Word2Vec, GloVe, etc.).

Proficient in statistical analysis and using tools like Pandas, NumPy, and Scikit-learn.

Familiarity with MLOps practices for version control, CI/CD, and containerization (Docker, Kubernetes).

Prior experience in developing conversational AI, chatbots, or virtual assistants using LLMs.

Experience in the healthcare sector, with a focus on processing domain-specific language or medical text.

Contributions to research publications or open-source NLP projects is a plus.

Strong problem-solving skills and the ability to work with unstructured data.

Excellent communication and collaboration skills.