Top Hybrid Data Analysis Jobs

Lead and execute performance marketing analytics: set analytical agenda, integrate disparate data, run root-cause analyses, build models, produce executive narratives, mentor team members, and drive cross-functional decisions while remaining hands-on with data, dashboards, and measurement.

Lead analytical work to identify business challenges and recommend solutions across product, marketing, and credit risk. Build models, perform SQL-based analysis, support go-to-market strategies, drive implementations with cross-functional partners, and monitor outcomes to improve profitability.

Ten-week, full-time summer internship applying analytical and strategic skills to business problems. Interns use data, modeling, and technology (e.g., SQL, Python) to drive recommendations, collaborate cross-functionally, and develop business acumen while being mentored and evaluated for full-time rotational program opportunities.

The Senior Associate will analyze business needs, translate them into technical requirements, and collaborate with teams on enterprise-scale tech solutions, primarily in life insurance operations.

The Senior Data Scientist will lead analytics projects, define success metrics, run experiments, and present data-driven insights to influence product and business strategies.

The Senior Data Analyst for Payments will analyze and optimize payment processes, collaborate with cross-functional teams, and ensure regulatory compliance to improve financial operations.

Lead analytics strategy and decision support across Product, Growth, Revenue, Operations, and Finance. Perform deep-dive analyses, design experiments, build predictive models and ML/AI prototypes, create trusted reporting and dashboards, ensure data quality, automate workflows, and partner with stakeholders to drive measurable business outcomes. Act as a primary analytics partner, prioritize work, communicate recommendations, and mentor analysts informally.

Lead hypothesis-driven analyses of complex claims, payment, employee, and vendor data to detect fraud signals, generate investigator-ready leads, improve lead lifecycle, partner with investigations and modeling teams, and deliver actionable recommendations to reduce loss.

Lead development of scalable econometric and causal-inference methods (DiD, RDD, IV, synthetic controls, panel data) to measure long-term marketplace impact. Integrate causal insights into production ML systems, influence product strategy, present findings to executives, and mentor data scientists and economists.

Build and maintain an enterprise fraud repository, validate and audit fraud datasets, analyze large structured datasets to identify fraud and credit risk trends, produce analytics and reports, use SAS/SQL for extraction and predictive modeling, develop risk controls and KPIs/KRIs, and support risk-based decisioning across consumer lending products.

Analyze large, complex datasets to detect and mitigate payment fraud. Develop and implement fraud strategies, metrics, detection tools, and present findings to senior stakeholders.