Top Hybrid Data Science Jobs

Develop and deploy machine learning models, design experiments and A/B tests, productionalize data science code with GitHub and MLOps pipelines, and collaborate with data vendors. The role offers hands-on experience with cloud infrastructure, advanced machine learning tools, and production AI systems while working alongside senior engineering and data science leaders.

Lead predictive modeling and causal inference initiatives for clients, developing models from exploratory analysis through production deployment, monitoring, and retraining. Apply GLMs, econometric methods, uplift modeling, propensity methods, and deep-learning forecasting. Process large-scale data with SQL and Spark, build models in Python, improve production reliability, and communicate findings to technical and business stakeholders in a consulting environment.

Build and own marketing measurement, experimentation, attribution, dashboards, and insights across paid, influencer, UGC, and lifecycle channels. Analyze CAC, ROAS, payback, cohorts, LTV, campaign performance, and incrementality. Debug tracking gaps, develop attribution taxonomies and instrumentation, run tests, and translate findings into clear recommendations for marketing, product, data, and finance leaders.

Improve Entrata’s AI models through foundation-model fine-tuning, training-data curation, evaluation frameworks, benchmarking, experimentation, and error analysis. The role translates business problems into measurable machine-learning objectives, assesses model safety and reliability, and partners with machine-learning engineers to productionize successful approaches. Responsibilities include working with instruction, preference, and synthetic datasets and communicating findings to technical and business stakeholders.

Lead end-to-end data science initiatives for commercial property or homeowners insurance: acquire and prepare data, build and validate predictive models, deploy and monitor production models, ensure governance and documentation, advise teams, and evaluate emerging ML/AI techniques to improve pricing, risk selection, and underwriting outcomes.

Lead data science for a new 0->1 B2B product: define strategy, frame high-impact questions, run experiments, design metrics/telemetry and dashboards, align global analytics, and translate insights into product decisions.

Own network cost unit economics by building and optimizing plan configuration models, validating them against carrier invoices and financial data, forecasting usage and spend, and supporting pricing and contract decisions. Move recommendations into automated, auditable workflows, maintain dbt and Redshift data models, investigate discrepancies, and communicate quantified business impacts.

As a Senior Staff Data Scientist, you will lead data science initiatives, mentor teams, develop frameworks for experimentation, and collaborate across departments to drive decision-making through data insights.

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 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.