
Lead Mortgage Data Scientist
4 days ago
We are seeking a seasoned professional to lead our data-driven initiatives and drive business growth through informed decision-making.
Key Responsibilities:
- Data Analysis and Preprocessing: Conduct thorough analysis and preprocessing of diverse datasets relevant to the mortgage industry, ensuring high-quality data for model training.
- Model Development and Fine-Tuning: Research and implement cutting-edge NLP models, focusing on pre-training as well as instruction tuning pre-trained LLMs for mortgage-specific applications.
- Algorithm Implementation: Develop and optimize machine learning algorithms to enhance model performance, accuracy, and efficiency.
- Collaboration: Work closely with domain experts to incorporate industry knowledge into model development, ensuring outputs are relevant and actionable.
- Experimentation: Design and conduct experiments to validate model hypotheses, analyze results, and iterate on model improvements.
- Documentation: Maintain comprehensive documentation of methodologies, experiments, and results to support transparency and reproducibility.
- Ethics and Bias Mitigation: Ensure responsible AI practices by identifying potential biases in data and models, implementing strategies to mitigate them.
Requirements:
- Technical Expertise: Strong background in machine learning, deep learning, and NLP. Proficiency in Python and experience with ML frameworks such as TensorFlow or PyTorch.
- NLP Knowledge: Experience with NLP frameworks and libraries (e.g., Hugging Face Transformers) for developing language models.
- Data Handling: Ability to handle large datasets, feature engineering, and statistical analysis.
- Problem Solving: Strong analytical skills with the ability to solve complex problems using data-driven approaches.
- Communication: Excellent communication skills to effectively collaborate with technical teams and non-technical stakeholders.
Preferred Qualifications:
- Educational Background: Master's or Ph.D. in Data Science, Computer Science, Statistics, or a related field.
- Cloud Computing: Familiarity with cloud platforms (e.g., AWS, Azure) for scalable computing solutions.
- Ethics Awareness: Understanding of ethical considerations in AI development, including bias detection and mitigation.
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