Senior Data Scientist, Machine Learning

First National Bank of Omaha•Omaha, NE

1d•Hybrid

About The Position

The Senior Data Scientist, Machine Learning is responsible for the development, implementation, and operationalization of machine learning (ML) and artificial intelligence (AI) models to drive business impact. This role involves working with a team of highly quantitative data scientists and working with a cross-functional team of engineers, analysts, and business users to deliver innovative solutions, optimizing decision-making processes, and enabling automation at scale. The role will ensure that models are effectively managed in accordance with the model risk management governance and integrated into business workflows and deliver measurable value.

Requirements

Master’s degree or higher in Data Science, Computer Science, Statistics, Mathematics, AI, or a related field.
Minimum of 3 years of experience in data science or related fields.
Proven track record of developing and deploying ML/AI models that deliver business impact.
Experience managing cross-functional teams and complex projects.
Proven experience to effective communicate modeling approaches and results to external regulators and proactively address their suggestions and comments.
Expertise in machine learning, deep learning, and statistical modeling techniques.
Proficiency in programming languages such as Python, R, and SQL, as well as ML/AI frameworks like Claude.
Strong knowledge of data visualization tools (e.g., Power BI) and cloud platforms (e.g., AWS, Snowflake).
Exceptional leadership, communication, and stakeholder management skills.
Analytical and strategic thinking abilities with a focus on measurable outcomes.
Candidates must possess unrestricted work authorization and not require future sponsorship.

Responsibilities

Oversee the end-to-end lifecycle of ML/AI models, from ideation and development to deployment and monitoring, including but not limited to loss prediction, fraud detection, marketing optimization and customer feedback mining.
Ensure the scalability, reliability, and robustness of deployed models.
Establish best practices for model development, including feature engineering, hyperparameter tuning, and model evaluation.
Work with key stakeholders (e.g., FNIT, business partners) to implement the model and drive business value at a timely manner.
Define KPIs to measure the model performance and impacts.
Implement monitoring systems to ensure model accuracy, relevance, and efficiency over time.
Continuously refine and optimize models based on feedback and performance metrics.
Ensure the model development and monitoring practices in compliance with the FNNI model risk management team.
Effective communicate modeling approaches and results to external regulators and proactively address their suggestions and comments.
Monitor and mitigate risks associated with model bias, drift, and interpretability.
Maintain transparency and accountability in the deployment of ML/AI solutions.
Evaluate and implement cutting-edge tools, frameworks, and platforms for ML/AI development and deployment.
Stay updated on emerging trends, technologies, and best practices in data science.
Drive experimentation and pilot programs to test innovative approaches and solutions.
Partner with business units to understand their challenges and deliver tailored modeling solutions.
Effectively deliver business values to key stakeholders through innovative ML/AI models.
Effectively communicate complex technical concepts to non-technical stakeholders in a clear and actionable manner.
Build and maintain strategic relationships with external partners, vendors, and research institutions.