Hiring | Senior Consultant Data Scientist

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.