Top Software Data Science Jobs

Design, train, and evaluate deep neural networks, foundation models, vision-language models, and AI agents for telecom use cases. Reproduce research-paper methods, run scalable experiments on cloud platforms or GPU clusters, and collaborate with researchers and engineers to move proofs of concept into production-ready solutions for cellular networks.

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Develops data workflows, statistical models, and machine learning solutions using tire, vehicle, simulation, telemetry, testing, and race data. Analyzes tire performance, operating windows, degradation, and strategy impacts while delivering dashboards, reports, notebooks, and decision-support tools. Partners with engineering, data, and motorsports teams, leads complex analytics projects, establishes reproducible modeling practices, and mentors engineers and analysts.

Conduct statistical analysis, evaluate product experiments, analyze user interaction data, identify growth opportunities, and build dashboards, notebooks, or AI agents. Partner with data science, engineering, and business teams to communicate actionable insights. The internship requires full-time participation for 12 weeks in the Seattle hub and enrollment in a master's program with graduation by August 2028.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Senior Data Scientist partnering with Product, Engineering, Design, Operations, and Clinical teams to define metrics, design experiments, apply causal inference, analyze funnels and cohorts, and build predictive models. The role translates complex data into actionable product and business recommendations, operationalizes validated insights, and establishes standards for experimentation and analytical quality across patient, payer, outcomes, and provider-focused teams.

Early-career data scientist supporting sector investment teams: deliver data pulls, analyses, trackers, dashboards, anomaly investigations, and reusable Python/SQL tooling. Use time-series methods, alternative data, and LLMs to accelerate workflows and build commercial judgment with investors.

Research intern supporting advanced analytics and new product development for revenue management. Responsibilities include preparing and analyzing data, identifying problems, designing and implementing solutions, researching forecasting, pricing, optimization, generative AI, and machine learning applications, presenting findings, and producing technical documentation. The role involves Python, SQL, Pandas, LLMs, RAG, and prototyping with tools such as LangChain and visualization frameworks.

Own product analytics and data science for a business-critical product area. Develop production-ready churn, resurrection, and targeting models; define metrics; analyze experiments; apply causal inference; and deliver actionable recommendations with quantified business opportunities. Partner closely with Product, Engineering, Design, and Growth teams while contributing to experimentation methodology, standards, and tooling.

Leads a 12-person data science and machine learning organization, setting strategy for predictive modeling, causal inference, AI evaluation, and ML infrastructure. Partners with product, engineering, and business leaders to apply data science to growth, retention, product optimization, support allocation, and risk modeling. Builds team structure, processes, technical standards, and culture while managing managers and senior technical contributors.