Top Hybrid Data Science Jobs

Lead end-to-end data science and machine learning initiatives, from identifying business use cases and querying data through prototyping, evaluation, deployment, and monitoring. Partner with Product, Engineering, and Machine Learning Engineering teams across areas such as risk, insurance, and payments. Develop predictive models, statistical methods, deep learning, and LLM solutions while contributing reusable data assets, tooling, MLOps strategy, technical reviews, and data-informed business decisions.

Build machine learning and causal inference models for customer acquisition, conversion, lifetime value, marketing measurement, and budget optimization. Develop production decision systems, evaluate predictive and business outcomes, and estimate incremental marketing impact using experimental and observational data. Partner with Marketing, Product, Engineering, and Sales to deploy and improve models that support profitable growth.

Leads end-to-end data science projects, including problem framing, data exploration, feature engineering, model development, evaluation, deployment, and communication. Applies statistical analysis, experimentation, and machine learning to business problems across marketing, underwriting, actuarial, operations, and fraud prevention. Partners with engineering and cross-functional stakeholders to deliver scalable analytical solutions, actionable insights, process improvements, and enhanced model performance.

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.

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.

Own data science for payment performance, optimization, timing, retry strategies, monitoring, experimentation, and fraud detection. Build payment-health scorecards, anomaly monitoring, payment-risk models, and production ML systems using Python, SQL, PySpark, Databricks, and MLOps tooling. Partner with Engineering, Product, and Risk to shape payment strategy and roadmap while applying causal inference, feature engineering, and production model monitoring.

Develop and deploy machine learning models and data pipelines for automated decision-making. Responsibilities include collecting and transforming data, exploratory analysis, predictive modeling, production deployment, model serving infrastructure, monitoring drift and performance, and communicating insights to stakeholders. The role requires strong Python and SQL skills, experience with cloud platforms and MLOps tooling, and collaboration across global teams.

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.