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About the Role
We are looking for a Senior Data Scientist to develop and apply modern machine learning and artificial intelligence across a range of products and business problems. The ideal candidate combines strong computer science foundations, rigorous experimental judgment, and the ability to turn advances in AI into reliable systems with measurable business impact.
The work spans document intelligence, multimodal systems that combine text, images, and structured data, and the 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.
You will work across the full machine learning lifecycle: framing problems, developing datasets, building and evaluating models, running large-scale experiments, and partnering with engineering teams to bring successful solutions into production. A central part of the role is turning successful approaches into reusable AI capabilities, patterns, and components that can support multiple teams and use cases rather than creating a separate one-off solution for every problem.
What You’ll Do
Identify high-impact opportunities for machine learning and AI in partnership with product, engineering, operations, and business teams.
Design, train, fine-tune, and evaluate machine learning and deep-learning models.
Work with foundation models across language, vision, and multimodal applications.
Develop robust datasets, features, training pipelines, and evaluation frameworks.
Run efficient experiments on GPU infrastructure and improve model quality, speed, and cost.
Compare alternative modeling approaches and make clear recommendations based on empirical results and business impact.
Build reusable modeling components, evaluation frameworks, and implementation patterns that accelerate work across teams and projects.
Establish clear interfaces and standards that allow other teams to adopt proven AI capabilities without rebuilding the underlying modeling work.
Build models and supporting systems that are reproducible, testable, and suitable for production use.
Partner with ML and software engineers on deployment, monitoring, reliability, and ongoing model improvement.
Communicate results, limitations, and tradeoffs clearly to both technical and non-technical stakeholders.
Stay current with advances in machine learning and determine when emerging techniques are mature enough to create practical value.
What We’re Looking For
Bachelor’s or advanced degree in Computer Science, Machine Learning, Electrical Engineering, Applied Mathematics, or a related technical field, or equivalent practical experience.
5+ years of experience developing machine learning solutions, including experience 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 the 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 a 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.
What Success Looks Like
You turn ambiguous business needs into well-defined machine learning problems and measurable success criteria.
You build models that improve real customer or operational outcomes, not only offline metrics.
You make thoughtful tradeoffs among model quality, complexity, latency, reliability, and cost.
You create reusable foundations that allow multiple teams to apply proven AI capabilities to new problems more quickly and consistently.
You help raise the organization’s technical standards for experimentation, evaluation, and production AI.
Identity Verification Notice: To ensure the security and integrity of our hiring process, all candidates are required to complete a secure identity verification. This process uses biometric and device validation to confirm identity and liveness. All data is handled securely in accordance with our privacy policy.
Kiavi takes a market-based approach to pay, and pay may vary depending on your location. The expected annualized base pay range for this full-time role to be performed remotely is $175,000 - $225,000 base + bonus + benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries. Within the range, the successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. Your recruiter can share more about the specific base pay range for your preferred location during the hiring process. Please note that the pay range listed above reflects the base salary only, and does not include the target value of bonus or benefits.
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