Top Software Data Science Jobs

Develops scalable self-service analytics, data science frameworks, predictive and causal models, experimentation strategies, semantic layers, and AI-powered analytics tools. Partners with Product, Engineering, Design, and data engineering teams to improve product adoption, engagement, retention, and monetization insights. Leads complex analytics initiatives, ensures analytical rigor and governance, communicates findings to executives, and mentors analysts and data scientists.

Evaluates and improves statistical models, machine learning algorithms, and analytic workflows supporting classified Space and Counterspace intelligence missions. Analyzes complex datasets, validates model performance, assesses data quality, recommends enhancements, integrates new analytic approaches, and prepares technical assessments for government stakeholders. The role collaborates with engineers, developers, and data science teams in a classified environment while ensuring security and data governance compliance.

Lead end-to-end development and deployment of predictive models, conduct advanced data-driven research, and solve ambiguous business problems. Partner with senior stakeholders and engineering leaders to shape strategic planning, resource allocation, and product innovation. Communicate complex findings to technical and executive audiences, architect scalable analytical solutions, mentor data scientists, and establish high standards for data science practices.

Lead product-focused data science and machine learning initiatives, including exploratory analysis, production model development, evaluation frameworks, generative AI, graph-based machine learning, recommendation systems, and personalization. Collaborate with engineering, product, and business teams to deliver high-impact solutions, influence strategy, and communicate insights to technical and non-technical stakeholders. The hybrid role requires four days per week in the Greater Boston area.

Lead advanced data science for Unity’s advertising business, analyzing revenue, supply, demand, and customer behavior. Define metrics, evaluate ML and product initiatives, guide roadmaps, and turn large-scale data into executive-level recommendations. Partner with Product, Engineering, Sales, and Finance, while mentoring data scientists and providing technical leadership without formal management authority.

Lead the Data Science function for an AI video product, setting priorities, guiding data scientists, analysts, and engineers, and solving high-impact analytical problems. Drive product, growth, experimentation, retention, monetization, and data-quality initiatives. Build reusable metrics, datasets, self-service tools, AI evaluation systems, and agentic analytics capabilities while partnering with Product, Growth, Finance, Engineering, and AI teams.

Own product-led growth analytics across activation, engagement, conversion, monetization, and retention. Lead experimentation, statistical analysis, causal inference, metric development, and proactive opportunity discovery. Build scalable analytical systems, monitoring frameworks, semantic models, and AI-powered tools. Partner with Product, Engineering, and leadership to shape product bets, investment decisions, and roadmap priorities. Work autonomously in a remote-first environment and communicate complex findings through clear, actionable recommendations.

Data Scientist partnering with cross-functional teams to define metrics, design experiments, uncover insights, build analytical models, and develop data tools and systems. The role owns complex data projects end-to-end, supports product, marketing, finance, and platform initiatives, and promotes data quality and accessibility. Candidates need strong SQL, scripting, distributed data systems, statistical methods, experimentation, forecasting, and stakeholder communication skills.

Develop and maintain A/B experimentation and evaluation platforms for Glean’s AI products. The role designs rigorous statistical methods, evaluates LLM capabilities across tasks such as summarization, reasoning, tool use, coding, and system design, and establishes best practices for metrics, benchmarking, and experimentation. Responsibilities include productionizing scalable tools, analyzing product data, defining KPIs and guardrails, and partnering with engineering, product data science, product management, and design teams.

Develops and evaluates machine learning models for United States Air Force mission problems. Responsibilities include data preparation, feature engineering, statistical analysis, model training and validation, performance monitoring, data governance, DevSecOps, compliance documentation, and dashboard development. Collaborates with government stakeholders, engineers, cybersecurity personnel, architects, and mission users while maintaining auditable model lifecycle records and adhering to TS/SCI security requirements.