Senior Applied Measurement & Data Scientist

Carnegie Mellon University•Pittsburgh, PA

9h•Onsite

About The Position

The SEI’s Applied Measurement & Experimentation (AME) team develops analytic workflows, measurement tools, causal inference capabilities, and robust data pipelines that support engineering and mission‑focused decision-making. We work closely with subject matter experts and mission stakeholders to produce reliable, reproducible, and trustworthy analytic solutions. Our mission is to help government and industry partners integrate evidence-based insights into high impact decisions by combining statistical rigor, modern data engineering practices, and emerging AI Software Lifecycle Management capabilities. You will help build tools, shape measurement workflows, and deliver analytic insights directly to decision-makers to support mission and engineering outcomes. AME’s decision‑directed research combines classical statistical methods with AI-supported approaches to produce data workflows, measurement and analytic insights that inform mission and engineering choices. In AME, you’ll work with engineers, mission operators, and analytics experts who rely on trustworthy measurement systems to make high impact decisions. If you value statistical rigor, engineering discipline, and practical analytics, this role lets you shape the evidence leaders use every day. You’ll collaborate with people who bring deep mission and technical expertise to build the measurement tools and analytic workflows that guide decisions across government and industry. It’s a role for someone who wants their work to matter and who values clarity, reproducibility, and teaming with domain experts to produce reliable insights for decision making.

Requirements

BS with 10+ years, MS with 8+ years, or PhD with 5+ years in data science, statistics, machine learning, computer science, or another quantitative field.
Proficiency in statistical modeling and data science using R or Python.
Experience with Linux/Unix, containerization, or modern data engineering tools, or willingness to learn.
Strong communication skills and ability to present analytic concepts to expert and non‑expert audiences.
Willingness to travel (up to ~25%) to CMU/SEI sites, customer locations, and conferences.
You will be subject to a background investigation and must be able to obtain/maintain a DoW security clearance.

Nice To Haves

Innovative and inquisitive with ability to imagine novel analytical solutions to problems
Ability to design and evaluate metrics that support trade‑off analysis, prioritization, and resource allocation.
Ability to produce clear, action‑focused analytic outputs, not just statistical summaries
Demonstrated ability to lead projects, coordinate multidisciplinary teams, manage complex analytic workflows, and deliver high-quality results.
Ability to participate effectively on teams, contributing technical expertise, supporting collaborative decision-making, and maintaining clear communication.
Strong experience applying statistical modeling, data science methods, and reproducible data engineering practices to mission-focused or real-world datasets.
Proficiency in R or Python for building analytic tools, dashboards, and reports.
Familiarity with (or ability to learn): containerization, infrastructure‑as‑code approaches, Linux/VM administration, relational and graph databases.
Ability to translate SME insights into structured analytic constraints and usable workflows.
Ability to communicate analytic concepts clearly to both technical and non-technical audiences.
Experience with causal inference concepts is welcome but not required; willingness to learn new analytic methods is essential.
Analytic/dashboard tooling such as Shiny, Dash, or similar frameworks.
Data engineering & infrastructure including pipelines, containerization, infrastructure‑as-code, and Linux environments.
Generative AI / Small Language Models including local deployment, Ollama, OpenWebUI.
Software engineering lifecycle practices for analytic tools.
Experience in U.S. Government / Department of War work and/or with FFRDCs, UARCs and National Labs is a plus.
Experience conducting decision directed analytic research, structuring questions, designing measurement approaches, and producing results that directly inform engineering or mission choices.
Experience publishing or presenting technical research.

Responsibilities

Lead analytic projects, including scoping work, and AI/ML enabled designing analytical approaches, coordinating interdisciplinary contributors, managing timelines, and ensuring high-quality technical outcomes that meet mission and engineering needs.
Collaborate on multi‑disciplinary efforts, working closely with colleagues and domain experts to refine workflows, build tools, and integrate statistical, machine‑learning, and small‑language‑model results into operational decision‑making.
Apply statistical modeling, ML and data science methods to complex real-world datasets, guiding customers in interpreting results and incorporating insights into mission and engineering decisions.
Build, maintain, and enhance analytic software tools including R/Python dashboard applications, analysis environments, automated AI/ML workflows, and robust data pipelines that support repeatable, reliable analytics.
Apply engineering discipline and scientific rigor to data pipelines, infrastructure, and operational analytics to ensure reliability, reproducibility, and trustworthy measurement.
Work with modern infrastructure tooling, learning new technologies as needed to ensure analytic systems operate smoothly and securely.
Explore and apply open‑source small-language model (SLM) and generative AI tools to enhance analytic workflows.
Contribute to research papers, technical writing, outreach materials, and present findings to conferences, workshops, internal teams, government customers, and senior leaders.