Top Data Science Jobs in Lincoln, NE

The Associate Data Scientist partners with clinical and operational teams to prepare and transform data, develop predictive models, apply statistical and AI techniques, and create actionable insights. Responsibilities include feature engineering, model optimization, data visualization, dashboards, stakeholder presentations, risk management, and Agile collaboration to improve patient care, hospital operations, and organizational outcomes.

Lead development and deployment of large-scale LLM and GenAI solutions for cybersecurity. Drive research strategy, model training, evaluation, safety/red-teaming, and cross-functional productization. Mentor data scientists, set technical standards, and represent the team through publications and thought leadership.

Develop and refine predictive models, perform exploratory data analysis, and generate AI-driven insights for DoD missions. Integrate multi-source data, apply geospatial analytics, iterate models via Agile sprints, produce performance and cost reports, and optimize analytic workflows for operational deployment.

Develop and maintain reproducible bioinformatics pipelines and multi-omics data products. Perform statistical modeling, ML, and integration of genomic, transcriptomic, spatial, proteomic, and clinical datasets. Build researcher-facing dashboards, notebooks, and APIs; ensure FAIR, containerized, version-controlled analyses; collaborate with scientists, engineers, and government stakeholders to enable reusable, secure, analysis-ready data workflows.

Designs AI risk stratification and evaluation methodologies for Veterans Health Administration systems, assessing performance, fairness, value, and post-deployment monitoring. Provides technical guidance on machine learning, healthcare data models, cohorts, synthetic data, and pilot evaluations. Develops risk management materials and supports governance decisions through quantitative analysis. Requires extensive applied data science experience, advanced education, and expertise in healthcare-focused machine learning.

Lead statistical modeling, AI/ML evaluation, data governance, data quality assurance, and executive analytics for VA EHR modernization. Build reproducible R-based analytics, reporting pipelines, dashboards, and governed data products using R, SQL, Python, Git, and cloud platforms. Evaluate model reliability, bias, drift, and operational value; integrate complex healthcare data; define metrics and validation rules; and advise stakeholders on auditable analytical practices.

Analyze, clean, and validate complex healthcare data; develop statistical, machine learning, and AI models; implement agentic workflows, RAG solutions, and model fine-tuning; deploy and monitor models in production; collaborate with technical and business teams; conduct research; and document and communicate findings.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

Build and deploy predictive models that drive partner action, analyze conversational and unstructured data using NLP techniques, and monitor model performance in production. Own AI data tooling, evaluation frameworks, in-warehouse model configuration, and reusable analytics foundations. The role also establishes modeling standards, evaluates fairness and limitations, collaborates with engineering and product teams, and prioritizes high-impact work across a lean data organization.

Leads enterprise manufacturing AI, machine learning, and Industry 4.0 initiatives across multiple plants. Develops and deploys solutions for predictive maintenance, process and quality optimization, energy efficiency, throughput, and OEE improvement. Establishes scalable data and AI platforms, governance, and MLOps standards while integrating industrial data sources. Leads and mentors data science teams, prioritizes projects, allocates resources, partners with cross-functional manufacturing stakeholders, and presents strategic recommendations and business cases to executives.

Senior Data Scientist partnering with Product, Engineering, and Design teams to analyze user behavior, product performance, and marketplace dynamics. Responsibilities include developing metrics and reporting frameworks, designing experiments, diagnosing business trends, analyzing marketplace behavior, and aligning stakeholders on analytical priorities. The role uses SQL and Python alongside BigQuery, dbt Cloud, Airflow, Tableau, and Statsig, with a focus on product analytics, experimentation, and data-informed decision-making.

Lead ecosystem-level data science and econometric analyses across Xbox, Game Pass, Console, PC, and Store experiences. Evaluate growth, engagement, monetization, experiments, incrementality, and investment ROI using statistical and causal inference methods. Build reusable analytical products and self-service capabilities, influence executive decisions, and mentor team members.

Develop and deploy statistical and machine learning models supporting credit risk, fraud, collections, and marketing. Responsibilities include data exploration, model design, testing, production monitoring, performance analysis, governance, and stakeholder communication. The role partners with Legal and Compliance, translates business questions into analytical solutions, researches new methods, and provides data-driven recommendations to leadership.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

Drive analytics and measurement for AI product adoption and impact. Build agentic analytics, small language models, predictive models, and customer-facing insights. Design and run experiments (A/B tests, causal inference), ship production models, and translate ambiguous business questions into actionable recommendations used by product, sales, and leadership.

Conduct data science research on experimental methodologies and causal modeling, develop tools to analyze experimental data, prototype improvements to platform features, and communicate results to internal teams and external stakeholders to drive product and research decisions.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Design, train, validate, and deploy traditional ML models and feature pipelines within Microsoft Fabric/Databricks. Partner with Data Engineers to optimize Medallion layers for model training and inference, implement MLOps (model registry, monitoring, retraining), perform EDA on large enterprise datasets, translate analytics to business insights, and document algorithm governance and fairness.

Leads product analytics for Xbox Growth Intelligence, using statistical, econometric, causal inference, experimentation, forecasting, and machine-learning methods to guide product investments. The role analyzes player behavior, engagement, retention, monetization, and ROI; builds experimentation and incrementality frameworks; develops reusable AI-enabled analytical tools; influences senior leaders; and mentors data scientists.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Design and deploy predictive forecasting and analytics to support Revenue operations. Build models for forecasting, churn, expansion, and pipeline analytics; create scalable dashboards; partner with Revenue Operations, Finance, and Data Engineering to improve data quality, KPI governance, and executive reporting to drive data-driven decisions and AI readiness.

Own end-to-end modeling for vendor matching, order routing, and supply-chain optimization. Build predictive, causal, RL and OR solutions; design autonomous decision logic; quantify counterfactual impact; ship production models with data engineering; mentor senior data scientists; write technical design docs; partner on quarterly priorities and expand into revenue cycle and finance domains.