Top Hybrid Data Science Jobs

As a Data Scientist at GLS, you will design data science solutions, develop predictive models, gather and clean data, and present insights to stakeholders while collaborating with business partners and IT.

As a Staff Data Scientist for Forecasting, you will develop forecasting algorithms, create data pipelines, mentor team members, and operationalize models for business needs.

The Data Science Actuary role involves building and maintaining rating systems and risk models, conducting analysis for underwriting and operations, and integrating new data sources while collaborating with multiple teams.

As a Data Scientist, you will develop data-driven solutions, create visualizations, and support data integration for energy projects applying machine learning and AI techniques.

The Sr. Data Scientist will utilize predictive models and data science techniques to optimize business outcomes, manage client relationships, and provide insights based on large datasets. Responsibilities include leading data science projects, developing models, and creating data visualizations.

Own end-to-end data science and ML work for B2C customers: build recommendation, forecasting, segmentation, and activation models; design A/B tests and causal analyses; create configurable multi-tenant model architectures; extract signal from sparse/noisy data; partner with engineering to productionize models and deliver analyses that change business decisions.

The Senior Data Solutions Partner acts as a liaison between stakeholders and the Data & Engineering organization, prioritizing analytics, automation, and technology use cases while ensuring alignment and governance throughout project delivery.

The Data Scientist will manage the data science lifecycle, collaborating with teams to create predictive models, analyze complex problems, and communicate insights to stakeholders.

Lead data science initiatives to enhance ticket fulfillment and customer experience. Develop predictive models, collaborate with teams, and ensure data quality for operational success.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.