Top Software Data Science Jobs

The Senior Data Scientist will analyze customer data, design experiments, build models, and support data-driven decision-making across product, marketing, and customer experience teams.

The Data Scientist will build AI-driven fraud and risk systems, work with real-world data, and leverage graph techniques to improve detection and labeling. They will partner with engineering to scale data systems in the compliance domain.

Work with product, engineering, and data teams to generate, label, and evaluate medication-focused datasets, review AI outputs for clinical accuracy, provide SME guidance for product features, ensure safety/compliance, and improve clinical intelligence processes and tooling.

The Data Scientist will analyze claims data, develop predictive models, and work with unstructured data using R and Python in a financial services context.

Design and curate multimodal, multitask datasets and run controlled training experiments to evaluate how data composition affects world-model performance. Build large-scale synthetic data pipelines, filtering and quality-control systems. Define benchmarks and evaluations, and partner with product and creative teams to translate target behaviors into data strategies that enable simulation-driven AI capabilities.

Lead a small data science team to build production algorithms and models across product and fair-play, partner with cross-functional teams to ship features, design cheating-detection systems, mentor data scientists, and communicate results to stakeholders.

As a Data Scientist at Sardine, you'll design and deploy data-driven solutions to prevent fraud, collaborating with clients and engineering teams to build and optimize machine learning models.

Lead development of Vanta's business analytics function: build scalable product data assets, define measurement frameworks, use AI-assisted analytics, partner with cross-functional teams, and deliver executive-level recommendations to drive product and business decisions.

Lead end-to-end analytical design and validation for Signals, Forecasting, Simulation, Predictions, and Cohort Detection. Develop causal inference, time-series forecasting, survival/retention, and clustering methods; document assumptions and validation; partner with engineers to productionize models; ensure statistical rigor, significance correction, and trustworthy outputs that translate into clear product experiences.

Partner with Product and Engineering to define product metrics, design experiments and causal analyses, build reporting and models, analyze user behavior and segments, and translate insights into product recommendations while establishing Product Data Science practices.