
Lead Data Scientist
2 days ago
Requirements:About the Role:
Key Responsibilities:
- Lead and mentor a team of data scientists to develop and deploy ML models, particularly in NLP, generative AI, and LLMs (e.g., GPT, BERT, LLaMA).
- Design, build, and implement end-to-end machine learning pipelines, from data preprocessing to production deployment.
- Collaborate with product and engineering teams to identify and scope machine learning opportunities aligned with business goals.
- Apply advanced NLP techniques (entity recognition, sentiment analysis, text summarization, etc.) to extract insights from unstructured data sources.
- Evaluate, fine-tune, and deploy large-scale language models for real-world applications such as chatbots, recommendation engines, document understanding, and automated decision systems.
- Drive innovation in data science methodologies and stay up to date with recent advancements in LLMs and generative AI.
- Conduct code reviews, set best practices for experimentation and model evaluation, and ensure model reproducibility and interpretability.
- Contribute to architectural decisions around model serving, feature engineering, and infrastructure.
- Communicate technical findings clearly to both technical and non-technical audiences.
- 7 years of hands-on experience in data science, with at least 3 years in a leadership or technical mentorship role.
- Expertise in Python and its data/ML ecosystem (NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc.).
- Proven experience in Natural Language Processing, including practical use of transformers, LLMs, and fine-tuning of pre-trained models.
- Strong grasp of ML theory and statistical modeling.
- Experience building and deploying machine learning models in production environments.
- Ability to lead projects from ideation to delivery in fast-paced, collaborative environments.
- Strong communication and stakeholder management skills.
- Bachelor's/Master's/PhD in Computer Science, Data Science, Statistics, or related field.
- Experience with vector databases (e.g., Pinecone, FAISS), RAG pipelines, and prompt engineering.
- Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps tools (MLflow, Kubeflow, etc.).
- Publications or contributions to open-source projects in ML or NLP.
Weekday AI
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