Tufin - Senior Data Scientist - Machine Learning

Key Responsibilities :

- Machine Learning Development : Design, develop, and implement advanced machine learning models primarily using tabular and text data.

- Business Impact Translation : Consistently translate complex research projects and analytical findings into tangible business impact, demonstrating clear ROI.

- Advanced ML & NLP : Apply expertise in Natural Language Processing (NLP), including working with open source Large Language Models (LLMs), developing recommendation systems, and implementing scalable classic ML algorithms (classifications and clustering).

- Similarity Metrics : Utilize and develop various similarity metrics for diverse data types.

- Statistical Analysis : Apply solid applied statistics skills, including descriptive statistics, distributions analysis, and statistical testing, to ensure robust model development and validation.

- Code Quality & MLOps : Write clean, efficient, and well-documented Python code. Engage with MLOps best practices for model deployment, monitoring, and lifecycle management.

- Collaboration & Communication : Work effectively as a strong collaborator within cross-functional teams, leveraging excellent interpersonal skills to communicate complex findings to both technical and non technical stakeholders.

Required Skills & Qualifications :

- 5+ years of experience in machine learning, specifically working with tabular and text data.

- Proven track record of translating research projects into quantifiable business impact.

- Proficient in Python with extensive experience across the following libraries and tools:

- GenAI/Deep Learning : TensorFlow, PyTorch, Hugging Face Transformers, Sentence Transformers, OpenAI API.

- Next-Gen Tools : LangChain, LlamaIndex.

- Data Science Essentials : Pandas, Scikit-learn (Sklearn), NumPy, SciPy, Matplotlib.

- NLP Essentials : NLTK, SpaCy.

- MLOps & Automation : MLflow, Kubeflow.

- Experience with NLP, open-source LLMs, recommendation systems, and scalable classic ML (classifications and clustering).

- Expertise in various similarity metrics.

- Solid applied statistics skills, such as descriptive statistics, distributions analysis, and statistical testing.

- MSc or PhD in Statistics, Computer Science, Mathematics, Electrical Engineering, or a related quantitative field is a significant advantage.