Top Software Data Science Jobs

Leads the data science function by managing and coaching data scientists, developing analytics methodologies, partnering with business leaders, and delivering predictive and prescriptive models. Drives adoption of analytical insights, educates stakeholders, manages vendors, recruits talent, and oversees team performance. Requires advanced expertise in statistics, machine learning, Python, R, SQL, Databricks, distributed computing, and cloud analytics.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Owns experimentation, business analytics, reporting, and advanced analysis for Realtor.com’s RDC Next platform. The role analyzes consumer and agent behavior, designs A/B and quasi-experiments, builds trusted datasets and dashboards, and delivers recommendations that influence product and business strategy. It also develops AI-enabled analytics workflows using LLMs, RAG, and agentic tools; partners with Product, Engineering, Finance, Marketing, and data teams; mentors analysts; and helps establish analytical standards and scalable data solutions.

Build and deploy quantitative models forecasting collectible market dynamics, asset valuations, pricing trends, demand, and capacity. Use proprietary transaction and grading data, supervised machine learning, time-series forecasting, backtesting, experimentation, and feature engineering. Partner with Engineering, Product, Finance, Operations, and domain experts to productionize reliable models with monitoring, explainability, and feedback loops. Deliver insights supporting pricing, product, go-to-market, grading, authentication, and business planning.

Senior Data Scientist responsible for partnering with business leaders to frame ambiguous problems, deliver statistical and machine learning solutions, communicate insights, and drive measurable adoption. The role includes end-to-end analytical ownership, stakeholder influence, executive communication, Agile backlog management, cross-functional collaboration with engineering and technology teams, mentorship, governance, and improving analytics maturity.

Lead data science initiatives supporting Product and Commercial Operations. Analyze growth opportunities, define success metrics, conduct experimentation and causal inference, and deliver actionable insights. Lead initiatives such as experimentation systems and data agents while promoting strong analytical practices. Partner with stakeholders to drive measurable product impact, minimize marketplace losses, and communicate complex findings to executive audiences.

Leads data scientists and machine learning engineers developing personalized customer experiences at scale. Sets team strategy, roadmaps, and priorities; partners with Product, Engineering, Marketing, Revenue, and Data Science leaders; guides machine learning systems through development, deployment, monitoring, and optimization; advances MLOps, software engineering, experimentation, and production reliability; and manages capacity, resources, technical standards, and team development.

Supports data science initiatives by mining and analyzing data, applying statistical, machine learning, deep learning, and generative AI techniques, and developing scalable business solutions. The role collaborates with senior scientists and cross-functional partners, contributes to predictive and prescriptive modeling, communicates findings, measures business impact, and participates in a structured Launchpad program with bootcamp training and technology rotations.

Lead research, design, and evaluation of large-scale detection models for automated, fraudulent, and malicious activity. Define measurement frameworks for noisy or incomplete ground truth, mentor data science practitioners, advance AI/ML techniques, and shape long-term detection strategy. Partner with ML Engineering, Data Engineering, and Product teams to influence roadmaps and protect Cloudflare customers from evolving threats.