Senior Applied Data Scientist (1099)

Protagonist is a company that specializes in data analysis and communication strategies, seeking a Senior Applied Data Scientist to join their team. The role involves designing and deploying data science solutions, transforming unstructured data into actionable insights, and collaborating with internal teams and clients to drive impactful decision-making.

Responsibilities:

• Design, develop, and deploy scalable, production-ready data science solutions that align with business and project goals
• Leverage AI, machine learning, and advanced statistical techniques to extract insights from complex, large-scale, or unstructured datasets
• Apply techniques such as classification models, clustering algorithms, and other AI methods to drive discovery and inform decision-making
• Design and implement codebook-driven analytical frameworks that translate qualitative research questions into reproducible computational workflows
• Build and iterate on applied models tailored to project and client needs
• Translate analytical findings into actionable recommendations, delivering measurable impact for both internal and external stakeholders
• Provide direct support to the Engineering team as needed to implement solutions
• Design and refine prompts, LLM chains, and agentic AI workflows that automate analytical tasks such as content classification, thematic coding, entity extraction, and structured summarization at scale
• Build and evaluate automated analytical products and pipelines that integrate large language models into repeatable, production-grade workflows with appropriate quality controls
• Experiment with emerging AI techniques—including retrieval-augmented generation (RAG), multi-step reasoning chains, and tool-using agents—to extend the company's analytical capabilities and accelerate delivery
• Write and maintain efficient, reproducible data pipelines using Python, R, SQL, or similar tools
• Perform complex data cleaning, transformation, and wrangling to ensure high-quality analytical output
• Create compelling dashboards and visualizations using Tableau, Superset, or other tools to communicate insights to both clients and internal teams
• Contribute to the design, refinement, and validation of analytical frameworks that structure how the company assesses complex information environments
• Bridge social science theory and computational practice, ensuring that analytical products are methodologically sound and defensible
• Collaborate with engineering and product teams to build integrated analytical solutions supporting the company's Narrative Analytics approach
• Partner closely with engineering to translate data science requirements into scalable, production-ready tools and pipelines
• Contribute to data architecture decisions, tool selection, and infrastructure development to ensure alignment between analytics and engineering capabilities
• Present data findings to clients with clear communication and actionable recommendations
• Contribute to business development efforts requiring data science expertise, including writing technical sections of proposals, white papers, and concept notes
• Lead improvements to internal data science workflows, methodologies, and quality assurance processes
• Mentor junior team members and interns, promote technical upskilling, and foster open discussion of best practices and emerging techniques in NLP, ML, and computational social science
• Contribute to the development and maintenance of shared analytical assets such as codebooks, scoring frameworks, and model evaluation protocols
• Demonstrate ownership, professionalism, and timely follow-through in all responsibilities and communications
• Thrive in a fast-paced, dynamic environment by adapting to shifting priorities and proactively identifying opportunities to improve processes and analytic contributions
• Communicate technical insights clearly and translate complex data science needs into actionable guidance for both technical and cross-functional teams
• Foster collaboration across teams with openness to feedback, respect for diverse expertise, and a commitment to continuous learning