Part-Time NLP Data Scientist for the DSI at Bar Ilan

The Bar-Ilan Data Science Institute (DSI):

Data is at the heart of contemporary research, from large scale biological experiments to online social dynamics, opening a window to understand, predict and influence our most pertinent systems. The dominance of Large Language Models (LLMs) has elevated Natural Language Processing (NLP) to the forefront of AI and Data Science applications, marking it as one of the most influential and prominent fields in the current landscape

The Bar-Ilan Data Science Institute links researchers who collect large-scale data, with others who develop tools for its analysis, providing a diverse platform for cross-disciplinary collaboration.

We are seeking an additional Data Scientist to join our Team, part time.

Position Summary:

Responsibilities:

• Apply advanced data analysis, statistical modeling, and machine learning techniques to extract insights from large and complex datasets.
• Develop predictive models and algorithms to address business challenges and support decision-making.
• Collaborate with software engineers to implement and deploy data science solutions into production.
• Collaborate with academic researchers to stay up-to-date with the latest advancements in data science and contribute to cutting-edge research.
• Engage with industry clients to understand their data-related needs and design customized solutions.
• Communicate findings and insights to both technical and non-technical stakeholders through visualizations, reports, and presentations.

Qualifications:

Additional Qualifications:

• Strong proficiency in programming languages like Python and R.
• Hands-on experience with data preprocessing, feature engineering, and model development.
• Familiarity with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and data manipulation libraries (e.g., pandas).
• Knowledge and experience of relevant techniques and skills that showcase your  expertise in the NLP domain, among others:
  • Text Preprocessing
  • Contextual Embeddings, e.g., BERT, GPT, Transformer-based models
  • Text Classification and Sentiment Analysis
  • Libraries and frameworks: NLTK, spaCy, Gensim, Transformers (Hugging Face)
  • LLMs, generative LLMs - Text Generation using Recurrent Neural Networks (RNNs) and Transformers
  • Autoencoder-Decoder Techniques
• Excellent problem-solving skills and the ability to think critically about complex issues.
• Effective communication skills and the ability to work collaboratively in cross-functional teams.
• Prior experience in working on data science projects with industry partners.