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Salary: USD 175,000 - 225,000 / annual
Kiavi is seeking a Senior Data Scientist to develop and deploy machine learning and AI solutions across products and business problems in financial services. The role spans the full ML lifecycle: problem framing, dataset development, model building and evaluation, large-scale experimentation, and production deployment.
Key responsibilities include identifying high-impact ML/AI opportunities in partnership with product, engineering, operations, and business teams; designing, training, fine-tuning, and evaluating machine learning and deep-learning models; working with foundation models across language, vision, and multimodal applications; developing robust datasets, features, training pipelines, and evaluation frameworks; running efficient experiments on GPU infrastructure to improve model quality, speed, and cost; comparing alternative modeling approaches and making empirical recommendations; building reusable modeling components and evaluation frameworks that accelerate work across teams; establishing clear interfaces and standards for AI capability adoption; ensuring models are reproducible, testable, and production-ready; partnering with ML and software engineers on deployment, monitoring, and reliability; communicating results and tradeoffs to technical and non-technical stakeholders; and staying current with advances in machine learning.
The work focuses on document intelligence, multimodal systems combining text, images, and structured data, and adaptation of foundation models using proprietary datasets. These models support complex workflows in regulated financial services, where accuracy, explainability, data governance, reproducibility, and careful evaluation are essential. A central part of the role is turning successful approaches into reusable AI capabilities and patterns rather than creating one-off solutions.
Success means turning ambiguous business needs into well-defined ML problems with measurable criteria; building models that improve real customer or operational outcomes; making thoughtful tradeoffs among model quality, complexity, latency, reliability, and cost; creating reusable foundations that allow multiple teams to apply proven AI capabilities; and helping raise the organization's technical standards for experimentation, evaluation, and production AI.
**Requirements:**
- Bachelor's or advanced degree in Computer Science, Machine Learning, Electrical Engineering, Applied Mathematics, or related technical field, or equivalent practical experience
- 5+ years of experience developing machine learning solutions, including taking models beyond initial experimentation
- Strong programming and software-engineering skills, particularly in Python
- Strong foundations in machine learning, statistics, algorithms, data structures, and experimental design
- Hands-on experience with deep-learning frameworks such as PyTorch or TensorFlow
- Experience training or fine-tuning transformer-based, vision, or multimodal models
- Experience working with GPUs and understanding practical constraints of model training and inference
- Ability to work with large, complex, and imperfect datasets
- Demonstrated ability to own open-ended problems from initial investigation through implementation and measurement
- Demonstrated ability to generalize successful solutions into reusable capabilities rather than one-off models
- Strong communication and cross-functional collaboration skills
**Preferred Qualifications:**
- Master's degree or PhD in Computer Science, Machine Learning, or related field
- Experience with distributed training, model optimization, quantization, or parameter-efficient fine-tuning
- Experience deploying and monitoring machine learning models in production
- Familiarity with cloud infrastructure, distributed data processing, and modern ML development platforms
- Experience building AI systems in regulated, risk-sensitive, or high-stakes environments
- Contributions to applied research, open-source projects, patents, or production AI products
