Top Software Data Science Jobs

Product Manager for content ranking responsible for defining strategy, roadmaps, and metrics for content recommendation and ranking. Partner with engineering, data science, legal, and policy to launch scalable, privacy- and safety-minded content experiences, measure impact, and iterate on product improvements to drive engagement.

Lead Data Scientist will define goals and metrics for algorithm Test & Evaluation, research T&E methodology, lead vendor coordination, present analyses and results to stakeholders, and transition algorithms into software and real-time embedded systems.

Lead Sprout's centralized Decision Analytics function to transform reporting into predictive, insight-driven decision-making. Set vision, operating model, and standards; manage managers, analysts, and data scientists across domain pods; partner with senior stakeholders; champion AI and ML integration; drive change management and build a high-impact analytics culture.

Leads business intelligence model, dashboard, metric, and report development for compliance analytics. Uses SQL, Python, R, VBA, statistical programming, predictive modeling, and advanced analytics to identify risks, trends, anomalies, and root causes. Translates complex data into actionable recommendations, communicates insights to technical and executive stakeholders, and supports compliance strategy and risk mitigation.

Develop and deploy machine learning models powering real-time bidding, advertising decisions, reporting, and customer predictions. Solve propensity modeling, bidding strategy, A/B testing, CLTV, and churn prediction problems at large scale. Build enterprise-grade data and machine learning platforms, write modular production code, collaborate with engineers, and own projects from concept through implementation.

The Applied Scientist role involves developing quantitative models for decision-making under uncertainty, focusing on pricing, demand modeling, and risk management. Responsibilities include building and optimizing models, collaborating with engineering and product teams, and translating complex problems into actionable solutions.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.

Lead end-to-end data science for lending products: define metrics, run experiments, build measurement systems, evaluate model and policy performance, use AI tools to accelerate analysis, and mentor the team while partnering with product, engineering, and risk stakeholders to drive credit product decisions.

Partner with Marketing to own attribution, ROI, conversion, and funnel analysis. Design experiments, surface proactive insights, optimize marketing spend, and collaborate with Data/Analytics Engineers to build data models and pipelines. Communicate findings and recommendations to non-technical stakeholders.

Lead data-science work for matching features: translate product goals into data problems, design and analyze A/B tests, perform exploratory analysis and data mining, generate strategic insights, and collaborate with AI, product, design, and engineering to develop and iterate production ML models.