Data Scientist

Nelnet•Lincoln, NE

1d•$80,000 - $115,000•Hybrid

About The Position

Nelnet is a diversified and innovative company with over 40 years of service, specializing in student loan servicing, professional services, consumer loan origination, payments processing, renewable energy solutions, and K-12 and higher education expertise. The company is seeking a talented Data Scientist to join its Data Science team. This role involves solving high-value business problems through advanced analytics, statistical modeling, and modern AI-driven solutions, with a focus on financial decision-making. The Data Scientist will blend financial modeling, data science, statistical analysis, and business strategy, partnering with stakeholders to analyze performance, build predictive and prescriptive models, improve forecasting, automate processes, and deliver decision-ready insights. The position offers exposure to technologies like Python, SQL, cloud platforms, BI tools, and emerging AI capabilities such as LLMs. Nelnet values curious, analytical, and self-directed individuals who can structure ambiguous problems and deliver practical solutions. US Citizenship and the ability to obtain a Federal security clearance are required. Nelnet operates on a hybrid work model, combining in-office and remote work, with a standard 24/16 hour (in-office/work-from-home) split for associates within 30 miles of an office.

Requirements

Bachelor’s degree required in a Quantitative or Programming discipline (e.g., Math, Physics, Finance, Economics).
2+ years of relevant experience in data analysis, modeling, or data science, OR completion of a bachelor’s/master’s degree program specifically in Data Science or a related field with strong applied project experience (for Data Scientist I).
5+ years of relevant experience in data science, analytics, modeling, or a closely related discipline (for Data Scientist II).
2+ years of hands-on programming experience, preferably in Python.
Experience working with large, complex datasets and modern analytics tools.
Strong analytical and problem-solving skills with the ability to synthesize complex data into clear recommendations.
Advanced proficiency in SQL and Python.
Experience working with database and ETL tools.
Experience building, testing, deploying, and monitoring predictive or statistical models in a business setting.
Strong understanding of statistical methods, modeling techniques, and quantitative analysis.
Experience with financial modeling, forecasting, scenario analysis, or related business modeling frameworks.
Experience with data pipelines, ETL processes, and relational databases.
Familiarity with cloud-based environments, preferably AWS.
Experience with business intelligence and visualization tools, preferably Power BI or ShadCN.
Excellent written, verbal, and interpersonal skills for coordinating across teams.
Ability to manage multiple priorities in a fast-paced environment with a high degree of ownership and accountability.
US Citizenship and the ability to obtain a Federal security clearance are required for this role.

Nice To Haves

Experience with JavaScript, especially visualization packages like ShadCN.
Polished presentation skills.

Responsibilities

Build, enhance, and validate financial and statistical models that support forecasting, performance analysis, and strategic decision-making.
Conduct deep-dive analyses to uncover trends, drivers, risks, and opportunities within financial and operational data.
Develop predictive models, algorithms, and analytical tools using Python and related data science libraries.
Work with large, complex datasets across SQL databases, AWS environments, internal systems, external APIs, and flat files.
Clean, transform, join, and engineer data to support robust, scalable modeling and analytics solutions.
Identify and evaluate new data sources from internal, external, and public datasets to strengthen models and analyses.
Create dashboards, visualizations, and reporting tools in Power BI or ShadCN to communicate insights clearly and effectively.
Present findings and recommendations to stakeholders at all levels, including senior leadership.
Improve team efficiency by automating manual processes and building reusable analytical workflows.
Stay current on emerging techniques in data science, financial analytics, machine learning, and AI.
Contribute to a collaborative team environment by mentoring junior team members and sharing best practices.