Top Software Data Science Jobs

Partner with Product, Engineering, and Go-To-Market teams to define success metrics, design experiments, evaluate product launches, analyze large-scale ad-tech data, and guide roadmap decisions. The role requires translating complex data science findings into actionable recommendations for technical and non-technical stakeholders while applying rigorous statistical, modeling, and experimentation practices.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

Conduct statistical analysis, causal inference, experimentation, predictive modeling, and machine learning using large datasets. Build data pipelines, dashboards, reports, and production models with Python or R, SQL, and Spark. Analyze fraud, financial crime, and business outcomes, communicate actionable recommendations, collaborate with multidisciplinary teams, and present findings to stakeholders.

Develop machine learning and data science solutions for financial crime risk management. Responsibilities include analyzing large datasets with SQL, Python, and PySpark; designing ETL pipelines; training supervised and unsupervised models; implementing solutions in Scala; applying LLMs and NLP; supporting customer proofs of concept; and collaborating with product and platform teams in an agile environment.

Analyze complex datasets using advanced SQL to measure expert performance, task quality, throughput, and operational efficiency. Build metrics, dashboards, and self-service analytics; identify data quality issues; and partner with Product, Operations, and other stakeholders to guide workflow improvements and business decisions. Translate ambiguous questions into actionable analyses and communicate findings to technical and non-technical audiences.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Lead data science for a core fintech product area, defining metrics, experimentation strategies, causal analyses, and models. Partner with product, engineering, design, and marketing to guide roadmap decisions. Build AI product evaluations, automated scorers, regression tests, and model monitoring practices. Contribute to company-wide data science standards, AI-enabled workflows, and measurement frameworks while mentoring teammates and communicating recommendations to senior audiences.

Design and deliver analyses, reports, and dashboards; translate technical findings for business leaders; perform complex data queries and reconciliation; identify data capabilities and partner with enablement teams; mentor analysts and drive a data-driven culture across functions.

Product Data Scientists will embed with iOS or web teams to define product metrics, analyze user behavior, design experiments, build statistical and machine learning models, and turn findings into actionable recommendations. They will create dashboards, experiment readouts, decision memos, and AI-powered analytics workflows while partnering with Product, Design, Engineering, and Data Engineering.

Conduct research on how consumer LLMs search, reason, and synthesize information; design experiments and causal inference analyses; support product and engineering decisions with rigorous measurement; and build data pipelines and recurring evaluations that turn evolving research findings into product capabilities.