Top Software Data Science Jobs

Annotate and review insurance policy and claim documents to build ML training data and evaluation rubrics. Tasks include locating policy limits, producing structured coverage conclusions, assessing claim coverage, liability, litigation stage, and damages. Work part-time as a US-based contract SME across excess and specialty lines.

Analyze complex datasets, develop dashboards and KPI reporting, apply statistical methods, improve data quality, and deliver actionable recommendations. The role partners with leadership and cross-functional teams on forecasting, business modeling, performance analysis, experimentation, and operational optimization. Responsibilities include querying, cleaning, validating, and transforming data; identifying trends, risks, and opportunities; and communicating findings to technical and non-technical stakeholders.

The Data Scientist will support intelligence programs by developing data-driven solutions, predictive models, and machine learning tools. Responsibilities include analyzing complex datasets, evaluating data sources, improving model and data accuracy, developing Python and API-based solutions, and partnering with stakeholders on business outcomes. The role requires SQL and large-scale data system experience, works independently, may mentor junior professionals, and is fully onsite at MacDill AFB.

Drive product-focused data science across growth, core product workflows, and incentives. Define metrics, run experiments, apply causal inference, build and monitor production ML models, and partner with engineering to ship data products. The role requires independently solving ambiguous problems, communicating findings clearly, and connecting analysis to user outcomes and product decisions.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Lead end-to-end data science projects to enhance Parafin's underwriting engine by developing predictive models, optimizing pricing, and collaborating with engineering teams.

The Data Scientist will design and implement machine learning algorithms for enterprise applications, collaborate with federal customers, and contribute to the C3 AI Suite's features.

Analyzes structured and unstructured data, performs exploratory and statistical analysis, builds dashboards and predictive machine learning models, and communicates insights to stakeholders. The role uses SQL, Python, BI tools, cloud data platforms, distributed processing, and modern AI/GenAI technologies. Responsibilities also include data quality, governance, pipelines, model deployment and monitoring, stakeholder collaboration, and translating analytical findings into business recommendations.

Build and own marketing measurement, experimentation, attribution, dashboards, and insights across paid, influencer, UGC, and lifecycle channels. Analyze CAC, ROAS, payback, cohorts, LTV, campaign performance, and incrementality. Debug tracking gaps, develop attribution taxonomies and instrumentation, run tests, and translate findings into clear recommendations for marketing, product, data, and finance leaders.

Build marketing data science models and decision systems for spend allocation, channel measurement, forecasting, experimentation, and growth optimization. Unify marketing, retail, revenue, and product data; develop dashboards and analytical products; apply AI/ML and causal inference; and translate complex analysis into actionable recommendations for executives and cross-functional teams. The role also includes improving data quality, mentoring, documentation, and technical leadership.