Data Scientist II at NPS Prism in Remote

NPS Prism
Title: Data Scientist II (Work From Home)
Company Profile:
NPS Prism is a market-leading, cloud-based SaaS business owned by Bain & Company. NPS Prism provides its customers with actionable insights and analysis that guide the creation of game-changing customer experiences. Based on rock-solid sampling, research, and analytic methodology, it lets customers see how they compare to their competitors on overall NPS®, and on every step of the customer journey.
With NPS Prism you can see where you’re strong, where you lag, and how customers feel about doing business with you and your competitors, in their own words. The result: Prioritize the customer interactions that matter most. NPS Prism customers use our customer experience benchmarks and insights to propel their growth and outpace the competition.
Launched in 2019, NPS Prism has rapidly grown to a team of over 200, serving dozens of clients around the world. NPS Prism is 100% owned by Bain & Company, one of the top management consulting firms in the world and a company consistently recognized as one of the world’s best places to work. We believe that diversity, inclusion and collaboration is key to building extraordinary teams. We hire people with exceptional talents, abilities and potential, then create an environment where you can become the best version of yourself and thrive both professionally and personally.
Position Summary:
We are seeking a Data Scientist II with experience in both classical machine learning (regression, classification, predictive modeling) and modern AI systems (LLMs, Generative AI and agentic frameworks).
The role requires strong applied modeling skills across structured and unstructured data, and experience deploying models into production environments.
This position involves end-to-end ownership of data science initiatives, from problem framing and experimentation to deployment and monitoring.
Key Responsibilities:
Develop and deploy AI solutions across ML and other genAI use cases.
Design, build, validate and optimize predictive models including regression, tree-based models, ensemble methods, gradient boosting and deep learning architectures where appropriate.
Develop and deploy LLM-based and Generative AI solutions, including retrieval-augmented generation (RAG), prompt engineering, fine-tuning and agentic workflows.
Build multi-agent systems and tool-integrated AI solutions for analytics automation, insight generation and conversational interfaces.
Apply statistical analysis, hypothesis testing and experimental design (including A/B testing) to inform business decisions.
Collaborate with cross-functional teams
Ensure production readiness through monitoring, model governance, performance tracking, retraining strategies and documentation.
Analyze structured and unstructured datasets to generate actionable insights.
Communicate analytical findings and technical concepts clearly to cross-functional stakeholders.
Contribute to code reviews, best practices and continuous improvement of AI infrastructures.
Independently manage projects in environments with evolving requirements and timelines.

Required Qualifications, Experience & Skills:
Qualifications:
Education:
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
Experience:
3+ years of professional experience in data science or applied machine learning roles.
Demonstrated experience delivering regression and/or classification models in production environments.
Hands-on experience building and deploying LLM-based or Generative AI applications.
Hands-on experience with agentic frameworks such as CrewAI, LangChain Agents, LangGraph or similar multi-agent orchestration frameworks.
Skills:
Strong proficiency in Python
Solid foundation in statistics, regression, classification and other classical ML techniques.
Hands-on experience with LLMs and Generative AI, including prompt engineering, RAG, fine-tuning and evaluation.
Experience building multi-agent systems using frameworks such as CrewAI, LangChain/LangGraph or similar.
Familiarity with vector databases and embedding pipelines.
Experience deploying ML/LLM solutions in production with MLOps practices (e.g., MLflow, CI/CD) on cloud platforms (AWS/Azure/GCP/Databricks).
Strong problem-solving and cross-functional communication skills.