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Hiring | Senior Consultant Data Scientist
4 weeks ago
Key Responsibilities:
- Data Analysis and Preprocessing: Analyze and preprocess diverse datasets relevant to the mortgage industry, ensuring data quality and relevance for model training.
Model Development and Fine-Tuning: Research and implement state-of-the-art NLP models, focusing on pre-training as well instruction tuning pre-trained LLMs for mortgage-specific applications. Utilize techniques like RLHF to improve model alignment with human preferences and enhance decision-making capabilities.
Algorithm Implementation: Develop and optimize machine learning algorithms to enhance model performance, accuracy, and efficiency.
Collaboration: Work with domain experts to incorporate industry knowledge into model development, ensuring outputs are relevant and actionable.
Experimentation: 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 are followed by identifying potential biases in data and models, implementing strategies to mitigate them.
Required Skills:
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: Proficiency in handling 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: Masters 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.
