Top Hybrid Data Science Jobs

Support the Data team by collecting, cleaning, and preparing datasets; perform exploratory analysis; help develop and evaluate ML models; build visualizations and dashboards; document findings; and collaborate with engineers and product teams while learning data science best practices.

As a Data Scientist, you will analyze complex data sets, build machine learning models, and work in a team to provide solutions, ensuring data security and optimization.

The Analyst will develop analytical solutions to enhance data quality and drive effectiveness in targeting and engagement using machine learning and advanced methodologies. They will collaborate to create innovative data products and solutions focusing on commercial channels, ensuring responsible design adhering to data integrity standards.

As a Founding Data Scientist, you will develop predictive models and optimize data infrastructure to enhance supply chain operations, focusing on shipment risk and decision-making.

Design, run, and analyze experiments (A/B/n) to optimize personalization, retention, and loyalty for Sportsbook marketing. Partner with marketing, ML engineering, and data teams to advance experimentation tools, automate analyses, and present actionable insights to stakeholders to inform promotional strategy.

The Data Science Engineer will develop and apply machine learning and data science algorithms for cybersecurity and power systems, designing solutions and collaborating with multidisciplinary teams to enhance critical infrastructure resilience.

The Data Scientist III will enhance cybersecurity operations using data science and machine learning techniques, developing analytical models, and integrating data from various sources to improve threat detection and monitoring.

As Lead Data Scientist, you will define data strategy, build AI-powered tools, drive product analytics, and scale systems to support company growth.

Own data and AI product lines including embedded BI, reporting, and AI-powered features. Define strategy, roadmap, and requirements; refine AI-generated workflows; translate business problems into data models and implementation-ready specs; partner with engineering, data, and customers; ensure data governance, quality, and alignment throughout product lifecycle.

Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.