Top Software Data Science Jobs

The candidate will analyze complex data to inform business decisions, design customer models, utilize machine learning tools, and collaborate across departments.

The Data Science Intern will collect and preprocess data, build machine learning models, and assist in deploying models while collaborating with team members.

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.

Join a talent network connecting senior/staff data scientists with VC-backed startups. Work with product and engineering to design experiments, build predictive and causal models, productionize analytics, create dashboards, mentor teammates, and shape data strategy to drive product and business outcomes.

As the first Data Scientist, you will build models and algorithms from scratch, impacting the company's success in optimizing insurance portfolios.

As an AI Applications and Data Science Engineer, you will develop AI applications, predictive models, NLP chatbots, and data automation solutions, enhancing user experiences and optimizing data processes.

Proactively explore RevenueCat data to identify customer problems and opportunities. Translate ambiguous product questions into analyses and production-grade predictive/descriptive models. Partner with Product, Engineering, and Analytics to shape roadmaps, define experimentation and benchmarking approaches, deploy and iterate on models powering customer-facing features, and communicate insights across technical and non-technical audiences.

Deliver end-to-end data science projects using machine learning and deep learning, collaborate with stakeholders, and support client engagement through analytical planning and solution design.

As a Data Scientist, you will collaborate with scientists to develop algorithms and software solutions for data analysis and machine learning, while engaging in multidisciplinary teamwork to meet project objectives.

Lead risk and decision science for a new credit product: define risk policy and launch strategy, build scalable analytics and dashboards, own unit economics, partner with product/engineering/compliance, and use experimentation and AI/LLM tools to drive policy and LTV improvements.