Top Software Data Science Jobs

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.

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.

Lead product analytics to improve user growth, engagement, retention, and revenue. Design and analyze A/B and multivariate experiments, perform funnel/cohort analyses, maintain KPIs and dashboards, ensure event tracking, leverage ML and AI tools for advanced analyses, and translate insights into product recommendations across cross-functional teams.

Own major product areas to convert human expertise into training data and tooling for AI. Conduct user research, write specs, build dashboards/MVPs, partner with engineering/research/ops/design, and drive product strategy from conception to execution.

Design and analyze experiments and causal studies, build retention measurement and predictive models, develop scalable PySpark/SQL data pipelines, create dashboards, and partner with product and growth teams to improve user engagement and retention.

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.

Support AI, analytics, and data modernization for VA research: perform ETL, statistical programming, reporting, dashboards, LLM-enabled analytics, data integration, quality validation, and executive-level presentations to improve operational decision-making.

Lead and grow a Data Science team to design, develop, and deploy production-grade AI solutions (LLMs, RAG, fine-tuning) aligned with product and business goals. Drive technical strategy, cross-functional collaboration, operational best practices, and responsible adoption of emerging AI technologies to deliver measurable clinical value.

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.

Analyze marketing, pipeline, and revenue data; develop actionable recommendations; build and deploy AI-agent workflows using business systems and frontier models; support marketing data quality and measurement strategy; and identify trends, anomalies, and optimization opportunities through automation and analytics.