Senior AI Data Scientist – Solutions Developer

• Gather, interpret, and manipulate structured and unstructured data to enable advanced analytical solutions for the business.
• Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
• Select the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
• Develop and deploy models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Composes, and assists peers with composing, technical documents for knowledge persistence, risk management, and technical review audiences.
• Assess business needs to propose/recommend analytical and modeling projects to add business value.
• Work with business and analytics leaders to prioritize analytics and modeling problems/research efforts.
• Build and maintain a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data.
• Translate complex business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations.
• Manage project milestones, risks, and impediments.
• Escalates potential issues that could limit project success or implementation.
• Develop best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards.
• Maintain expertise and awareness of cutting-edge techniques.
• Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.
• Serve as a mentor to junior data scientists in modeling, analytics, and computer science tasks.
• Participate in internal communities that drive the maintenance and transformation of data science technologies and culture.
• Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.

• Bachelor’s degree in mathematics, Computer Science, Statistics, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience; and
• 6+ years of experience in a

OR Advanced Degree (e.g., Master's, PhD) in mathematics, computer science, statistics, science and engineering, ai, or other similar quantitative discipline and 4+ years of experience in predictive analytics or data analysis.

• 4+ years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
• 4+ years of experience in

for performing statical analysis and/or building and scoring AI/ML models

• Experience writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
• Strong experience in querying and preprocessing data from structured and/or unstructured databases using

etc.

• Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics and understanding real-world constraints such as latency, cost, and reliability in AI solution designs.
• Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts across risk stripes, including experience in the documentation and

for risk management.

• Advanced experience with the concepts and technologies associated with

for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc.

• Advanced experience with the concepts and technologies associated

such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.

• Expertise in LLMs and agentic systems development with

or others, with proven experience including prompt engineering, tuning and post-training techniques, multi-agent systems, agent optimization and tool use, RAG and context optimization, and observability and monitoring.

• MLOps Integration experience in facilitating engineering implementation of production scaled AI solutions in partnership with dedicated AI Engineers in cloud environments such as AWS or GCP.
• Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.
• Experience guiding and mentoring junior technical staff in business interactions and model building.

• Financial services, insurance, banking, or other highly regulated industry experience.
• Experience with cloud-native application development and modernization initiatives.
• US military experience through military service or a military spouse/domestic partner