Top Software Data Science Jobs

Develops and evaluates machine learning models for United States Air Force mission problems. Responsibilities include data preparation, feature engineering, statistical analysis, model training and validation, performance monitoring, data governance, DevSecOps, compliance documentation, and dashboard development. Collaborates with government stakeholders, engineers, cybersecurity personnel, architects, and mission users while maintaining auditable model lifecycle records and adhering to TS/SCI security requirements.

Develop and deploy machine learning algorithms that improve real-time device identification and fraud detection. Own data science projects from experimentation through production, including supervised, semi-supervised, and unsupervised learning, real-time inference, training automation, exploratory analysis, and model-to-service integration. Contribute to engineering best practices, collaborate within a global remote team, and participate in a shared on-call rotation.

Build and maintain backend data architecture and operational pipelines for a secure geospatial imagery program. Develop, monitor, and sustain data movement across security domains, platforms, systems, formats, and interfaces. Manage cloud-based SQL and NoSQL databases, use APIs for data exchange, and develop pipelines with Python, JavaScript, or Java. Communicate technical architecture to engineering teams and government customers. Preferred experience includes ATO processes, NPE certificate management, SAFe, and pipeline health visualization.

Leads AI and machine learning strategy, projects, and teams across the full model lifecycle. Responsibilities include developing and deploying models, establishing governance and MLOps standards, evaluating emerging technologies, managing AI risks, mentoring technical staff, partnering with stakeholders, and reporting business impact and delivery progress to senior leadership.

Lead data science projects from ideation through enterprise impact, building scalable and well-tested solutions that improve healthcare outcomes. Apply probability, statistics, machine learning, SQL, and Python to develop data-driven products and insights. Partner with stakeholders, assess product performance, identify strategic improvements, mitigate risks, and mentor teammates while aligning initiatives with business objectives.

Own Workhelix’s analytical engine end to end, including customer data integration, ETL, data modeling, AI opportunity assessments, predictive analyses, visualizations, and production deployment. Build Python and SQL systems running in cloud infrastructure, develop customer-specific analyses, create internal analytical tools, and shape the data science roadmap. Partner with engineers, strategists, executives, and customers while setting standards for analytical rigor and translating research into practical business decisions.

Develop and deploy AI solutions from problem framing through production. Responsibilities include identifying AI opportunities, leading MVP development, building data pipelines, experimenting with generative LLMs, fine-tuning NLP models, and delivering scalable features. The intern will collaborate cross-functionally, communicate technical impact to business audiences, and work with cloud platforms such as AWS or Google Cloud.

Partner with Product, Engineering, and Go-To-Market teams to define success metrics, design experiments, evaluate product launches, analyze large-scale ad-tech data, and guide roadmap decisions. The role requires translating complex data science findings into actionable recommendations for technical and non-technical stakeholders while applying rigorous statistical, modeling, and experimentation practices.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

Conduct statistical analysis, causal inference, experimentation, predictive modeling, and machine learning using large datasets. Build data pipelines, dashboards, reports, and production models with Python or R, SQL, and Spark. Analyze fraud, financial crime, and business outcomes, communicate actionable recommendations, collaborate with multidisciplinary teams, and present findings to stakeholders.