Top Hybrid Data Science Jobs

Leads data science strategy for provider growth, including experimentation, marketing and lifetime-value measurement, forecasting, causal inference, predictive modeling, and product analytics. Manages and develops a data science team, creates roadmaps, partners cross-functionally, identifies provider growth opportunities, and translates complex analyses into executive recommendations. The role requires hands-on work with SQL, Python or R, data modeling, and dbt.

Lead data science for lead valuation, predictive close scoring, revenue optimization, and constrained allocation across a national marketplace. Build inventory valuation, pricing, forecasting, experimentation, simulation, and optimization models using methods such as mixed-integer and stochastic programming. Partner with Product, Pricing, Marketing, Finance, and Engineering to deploy models into production decisioning, monitor KPIs, and communicate tradeoffs to executive stakeholders.

Supports machine learning models for credit cards and personal loans by preparing data, developing features, evaluating data quality, monitoring model performance, conducting exploratory analyses, and documenting reproducible analytical work. Partners with data scientists, business teams, technology partners, data providers, and vendors while ensuring model governance, testing, controls, and risk requirements are met.

Analyze acquisition data and guide marketing strategy, budget allocation, channel optimization, and growth decisions. Design incrementality experiments, develop marketing mix and allocation models, define acquisition metrics, identify new growth opportunities, and improve web conversion. Partner with Marketing, Engineering, Finance, Design, and User Research to translate statistical and machine learning insights into measurable business outcomes across paid, organic, consumer, and enterprise channels.

Develops end-to-end data science solutions, including ETL, statistical and machine learning model development, validation, deployment, and impact measurement. Collaborates with cross-functional teams to implement production models, improve data quality, identify correlations, and communicate insights to business stakeholders. Requires Python, SQL, analytical modeling, and familiarity with Spark and Hive.

The Data Scientist will use SQL, Python, statistics, machine learning, and AI/LLMs to solve business problems involving customer behavior, marketing effectiveness, conversion, revenue, and forecasting. Responsibilities include data preparation, feature engineering, predictive modeling, experimentation, workflow automation, model deployment and monitoring, and communicating actionable recommendations to technical and business stakeholders.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines across lending products. Analyze large transactional datasets using Python and SQL, partner with Fraud Operations to refine models, reduce false positives and negatives, improve underwriting and verification, apply AI in production, and lead and mentor team members.

Leads advanced data science, machine learning, AI, predictive analytics, anomaly detection, and NLP solutions for Internal Audit. Develops production-ready models and analytical frameworks, supports risk identification and continuous monitoring, and partners with audit, technology, and data teams. Communicates insights to technical and business stakeholders, ensures governance and validation standards, and mentors team members. The role requires strong Python and SQL skills, large-scale data experience, and expertise in deploying and monitoring analytics solutions.

Performs data gathering, mining, processing, modeling, and customer analytics on large datasets. Applies NLP, text analysis, machine learning, and statistical methods to identify customer needs and generate business insights. Develops and optimizes algorithms, presents findings to management through data visualization, and supports requirements analysis and data-driven decision-making. The role also involves AI agent development, cloud technologies, version control, and Agile collaboration.

Leads enterprise data science and AI solution delivery, including ML, generative AI, and predictive model development, deployment, monitoring, governance, and integration into business workflows. Drives AI adoption, change management, value realization, and evaluation of emerging technologies while partnering with data, engineering, and platform teams. Builds and leads AI delivery teams and ensures compliance with security and governance standards.