Top Software Data Science Jobs

Designs and maintains data analysis, validation, and visualization solutions supporting audit plans. Extracts and cleans data from multiple databases, performs quality checks, develops Tableau dashboards, identifies risks, and presents actionable insights to audit teams. The role also gathers requirements, documents processes, optimizes analytics, collaborates across departments, and manages multiple projects under deadlines.

Develop and validate machine learning models, quantify model risks, challenge production models, and contribute to enterprise model governance. Analyze large-scale numeric and textual datasets using Python, AWS, Spark, and related tools. Partner with data scientists, engineers, and product managers, validate models across business domains, and communicate model-risk implications to executives.

Build, optimize, validate, and deploy machine learning models and AI agents for healthcare applications. Analyze healthcare data, conduct A/B experiments and statistical studies, establish scalable ML development and serving processes, research advanced AI methods, and present insights to leadership. Mentor and recruit AI/ML talent, guide best practices and ethics, and pursue intellectual property through cutting-edge AI research.

Develops AI and machine learning prototypes for insurance applications using Python, large language models, NLP, retrieval-augmented generation, agentic AI, information retrieval, and computer vision. Collaborates with data scientists and engineers on ideation, product design, algorithm development, data analysis, and experimentation in an R&D environment.

Lead high-impact consumer data science initiatives across experimentation, causal inference, analytics, search, personalization, and product measurement. Set technical standards and multi-quarter analytics roadmaps, mentor data scientists, develop AI-native workflows and self-service tools, and communicate recommendations to product, engineering, and executive stakeholders. The role requires independent ownership of ambiguous analytical problems and technical leadership without formal people management.

Build AI- and automation-driven analytics workflows for SEO and AEO marketing performance. Responsibilities include automating reporting, anomaly detection, root-cause analysis, data exploration, segmentation, visualizations, and insight generation. The role partners with analysts and stakeholders to translate business questions into reusable frameworks, refine prompts and agent logic, and develop scalable analytical tools using Python, SQL, GenAI, and marketing analytics platforms.

Develop and optimize generative AI and large language model solutions, including fine-tuning, agentic workflows, retrieval-augmented generation, and experimentation. Research emerging algorithms, evaluate model performance using automated and human-in-the-loop methods, implement safety guardrails, and integrate AI services with cloud platforms. Mentor junior data scientists and guide technical approaches for AI-driven products.

Lead AI, machine learning, and data science workstreams from methodology through production delivery. Develop scalable, maintainable analytical code, explore new methods, influence product decisions, and collaborate with engineering, product, business, and data teams. Translate technical tradeoffs into actionable recommendations, present findings to technical and business audiences, and deliver measurable business impact while managing risks and maintaining methodological quality.

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.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines using Python, SQL, machine learning, and large transactional datasets. Partner with Fraud Operations to refine models, reduce false positives and negatives, analyze emerging fraud trends, and improve underwriting and verification strategies. Communicate insights to senior stakeholders, apply AI in production, and lead team members and initiatives.