Applied Data Science Research Engineer - Talent Analytics
Mcchrystal Group· Management Consulting· Jun 16, 2026 · 4mo agoPosted 133 days ago
Alexandria, Virginia
In office
Full-time
Senior
$125K–$190K
4+ years exp
Before you apply
Ghost Signal:
Open since June 16, 2026 — 133 days, and still accepting applications.
Job description
Position Overview:
Our consultants work side by side with client organizations to help them make smarter decisions about their people. The Applied Data Science Research Engineer - Talent Analytics is the analytical engine behind that work.
In this role, you will be embedded on client-facing consulting teams, working directly with clients to answer complex questions about their workforce and talent landscape. The data you work with spans publicly available talent and talent-adjacent data, as well as clients’ own workforce data.
The questions you'll tackle might look like: How much should we be paying for this role in this market? Where does the talent we need actually exist, and can we compete for it? How does our workforce compare to our competitors? What skills does our organization have today, and what are we missing?
To answer those questions, you'll pull from a wide toolkit, writing code, scraping and acquiring data from public sources, applying natural language processing, machine learning, and AI techniques, and designing custom analytical approaches when no off-the-shelf solution exists. Every engagement is different, and the problems are genuinely novel.
This is not a role that maintains systems or runs recurring reports. It is investigative and project-based by nature. You'll move from engagement to engagement, working alongside consultants and client business leaders to develop proprietary methodologies, build analytical capabilities that don't exist anywhere else, and deliver the data assets and insights that help clients make better decisions about their talent, workforce, organization, and leadership.
Workforce & Talent Intelligence
Conduct research and analysis related to labor markets, compensation, talent availability, workforce composition, organizational structures, skills, and recruiting dynamics.
Develop methodologies to estimate or infer workforce attributes that are not directly observable.
Analyze talent pools, labor supply, competitive hiring environments, and organizational capabilities.
Produce actionable talent intelligence for client engagement s .
Communicate findings, assumptions, confidence levels, and limitations to both technical and non-technical audiences.
Research Engineering & Data Acquisition
Acquire data from public, commercial, and proprietary sources.
Develop custom web scraping, extraction, and enrichment workflows to support research initiatives.
Build one-off software tools and analytical applications required to answer specific business questions.
Evaluate data quality, completeness, and reliability across multiple sources.
Rapidly learn and apply new technologies , techniques, and datasets as project requirements evolve.
Machine Learning, AI & Advanced Analytics
Apply machine learning, natural language processing, statistical methods, and generative AI techniques to solve talent intelligence problems.
D evelop similarity, matching, classification, clustering, ranking, and inference approaches when appropriate .
Leverage large language models and modern AI tooling to accelerate research and insight generation.
Design experiments and validation approaches to assess analytical accuracy and reliability.
Translate analytical outputs into practical business recommendations.
Consulting & Collaboration
Partner with consultants, researchers, and client-facing stakeholders to understand business challenges.
Contribute to the development of proprietary talent intelligence methodologies and intellectual property.
Support client engagements through research, analytical problem solving, and technical expertise .
Present research findings and recommendations to clients and internal stakeholders.
Share tools, approaches, and best practices across the organization.
Qualifications:
Required Qualifications
Bachelor's degree in Computer Science , Data Science, Statistics, Economics, Mathematics, Engineering, Social Sciences, or a related quantitative field.
4-6 years of relevant experience
Experience conducting independent analytical or research-oriented projects.
Strong programming skills, particularly in Python.
Strong analytical reasoning and problem-solving abilities.
Ability to work effectively in ambiguous environments with limited precedent or direction.
Excellent written and verbal communication skills.
Experience with labor market, workforce, recruiting, compensation, or organizational data.
Experience working with large structured and unstructured datasets.
Must be able to obtain and maintain a U.S. Government security clearance.
Preferred Qualifications
Experience with web scraping, data acquisition, and information extraction.
Experience with machine learning, NLP, or AI-assisted analytics.
Exposure to consulting, market intelligence, economic research, competitive intelligence, or workforce analytics environments.
Application prep
Use the role’s language and application details while tailoring your materials.
Keywords to mirror
machine learningaipython
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