Blue Rose Research

Operations

• Work with leadership to define and execute the organization’s strategic business plan, translating high-level business vision into clear, actionable approaches
• Set and track OKRs/KPIs aligned across internal goals – including growth, impact, revenue, utilization, and operational efficiency
• Synthesize qualitative and quantitative data into clear recommendations that inform priorities and guide decision-making around business strategy
• Support the launch and scaling of products, including go-to-market strategy and pricing models
• Identify operational bottlenecks and inefficiencies of a growing organization, implementing solutions that enable effective day-to-day activities and equip the organization to grow efficiently
• Design and implement scalable organizational structures and staffing models that align with business growth across various scenarios
• Serve as bridge between leadership, operations, and research/client-facing teams
• Oversee financial planning and analysis, including budgeting, forecasting, and revenue modeling
• Develop financial scenarios for different pricing and business approaches
• Monitor financial performance, costs, and cash flow, identifying areas for improvement on an ongoing basis
• Build out go-to-market planning for new products and provide ongoing assessment of the market current products
• Partner with client teams on revenue planning, and timely, accurate invoicing, and advise on potential new pricing schemes and marketing approaches
• Lead regular business reviews with leadership, presenting insights and recommendations

Director, Business Strategy and Operations

Machine Learning Engineer

• Manage a team of senior data scientists focused on fine-tuning large language models, conducting cutting-edge R&D, and building production inference systems.
• Collaborate with senior leadership to define the team roadmap and align priorities with organizational goals.
• Lead weekly meetings and standups, keeping the team unblocked and execution moving forward.
• Provide technical direction across projects using open-weight and off-the-shelf LLMs, as well as other advanced ML techniques.
• Oversee experimentation, optimization, and data quality to ensure models are accurate, reliable, and production-ready.
• Foster creative problem-solving and methodological rigor when challenges require custom solutions beyond standard ML approaches.
• Translate complex model outputs into actionable insights for stakeholders, ensuring technical work drives real-world impact

Machine Learning Manager, LLM

Data Scientist

• Blue Rose Research develops progressive research products informing strategy for progressive organizations
• Work at intersection of modeling and analysis to turn modeled outputs into insights
• Build out analytics pipelines to produce automated reporting on public opinion and voter behavior
• Build clear data visualizations in R for less technical audiences
• Analyze data using regressions and other statistical concepts to answer questions from political strategists and clients
• Deliver actionable guidance to internal stakeholders
• Build subject matter context and flag potential data bugs for machine learning data scientists
• Provide product feedback to engineers and machine learning data scientists
• Contribute new ideas to analysis, interpretation, and communication of data

Data Scientist - Analytics, Senior and Junior

Research Analyst

• Designing and launching surveys that will be taken by hundreds of thousands of Americans
• Managing the fielding of multiple surveys simultaneously to ensure sufficient data collection on time and on budget
• Tracking timelines and priorities and liaising between data scientists, engineers, and client-facing stakeholders
• Supporting external outreach efforts, including presentations to clients, political partners, and other vendors and consultants
• Analyzing data to answer questions from political strategists and clients
• Building subject matter context and thinking critically about our data in order to discern real trends versus potential bugs to flag for our machine learning data scientists
• Contributing new ideas to the ways we analyze, interpret, and communicate our data
• Providing product feedback to engineers to improve our tools for internal and external usage