Senior Applied Measurement & Data Scientist

Carnegie Mellon University; Pittsburgh (Allegheny County); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Bus pass

Tuition benefits

Retirement plan

Life insurance

Job summary

Carnegie Mellon University’s Software Engineering Institute (SEI) in Pittsburgh, PA, seeks a data scientist to lead analytic projects, refine workflows, and deliver actionable insights to decision-makers. You will build tools and data pipelines, applying statistical modeling and ML to real-world datasets, while collaborating with experts across domains.

The role emphasizes reproducible analytics, modern data engineering practices, and the exploration of open‑source SLM and generative AI tools to

Qualifications

• BS/MS/PhD in data science, statistics, ML, CS, or quantitative field with substantial experience.
• Proficiency in statistical modeling and data science using R or Python.
• Experience with Linux/Unix, containerization, or modern data engineering tools; willingness to learn.
• Strong communication skills and ability to present analytic concepts to expert and non‑expert audiences.

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, refining workflows, building tools, and integrating statistical, machine‑learning, and small‑language‑model results into operational decision‑making.
• Apply statistical modeling, ML and data science methods to complex real‑world data, guiding customers in interpreting results and incorporating insights into decisions.
• Build, maintain, and enhance analytic software tools including R/Python dashboard applications, analysis environments, automated AI/ML workflows, and robust data pipelines.
• Apply engineering discipline to data pipelines, infrastructure, and operational analytics to ensure reliability, reproducibility, and trustworthy measurement.
• Work with modern infrastructure tooling and learn 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.

Skills

Statistical modeling

Data science

R/Python

Linux/Unix

Containerization

Communication skills

Travel readiness

Causal inference concepts

Education

BS with 10+ years in data science/quantitative field

MS with 8+ years in data science/quantitative field

PhD with 5+ years in data science/quantitative field

Tools

R

Python

Shiny or Dash

Containerization

Linux/Unix

Job description

What We Do

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.

What You Will Do

• 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.

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.

Knowledge, Skills, and Abilities

• 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.

Expertise in One or More of the Following

• 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.

Desired Experience

• 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.

Summary

This role is ideal for a data scientist who enjoys combining causal reasoning, analytics, software development, infrastructure support, and SME collaboration, while leading analytic projects and contributing effectively on teams.

Joining the CMU team opens the door to an array of exceptional benefits.

Benefits eligible employees enjoy a wide array of benefits including comprehensive medical, prescription, dental, and vision insurance as well as a generous retirement savings program with employer contributions. Unlock your potential with tuition benefits, take well‑deserved breaks with ample paid time off and observed holidays, and rest easy with life and accidental death and disability insurance.

Additional perks include a free Pittsburgh Regional Transit bus pass, access to our Family Concierge Team to help navigate childcare needs, fitness center access, and much more!

At Carnegie Mellon, we value the whole package when extending offers of employment. Beyond credentials, we evaluate the role and responsibilities, your valuable work experience, and the knowledge gained through education and training. We appreciate your unique skills and the perspective you bring. Your journey with us is about more than just a job; it’s about finding the perfect fit for your professional growth and personal aspirations.

Location

Pittsburgh, PAJob Function

Software/Applications Development/EngineeringPosition Type

Staff – RegularFull Time/Part time

Full timePay Basis

SalaryMore Information:

• Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.
• Statement of Assurance