Kearney Activate- Senior Data Scientist & Product Engineer

Kearney•Washington, DC

20d•$100,000 - $150,000•Hybrid

About The Position

As a Senior Data Scientist / Product Engineer at Kearney Activate, you will play a key role in delivering secure, cloud-hosted data and analytics solutions for clients within Kearney’s Mobility, Defense and Advanced Industrials practice. This role is designed for a senior generalist who can take a business question all the way from framing and exploratory analysis through modeling, deployment, and ongoing performance monitoring. You will work at the intersection of business, product, and technology, partnering directly with client stakeholders and mentoring junior technical teammates along the way.

Requirements

Applicants must be legally authorized to work in the United States at the time of application. This position is not eligible for employer-sponsored work authorization now or in the future, including H-1B visa sponsorship or sponsorship for any other employment-based immigration case.
Ability to obtain, or current possession of, a U.S. Secret security clearance; active or prior clearance is a strong plus.
Full-time employment.
Bachelor’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related field, or equivalent demonstrated experience; an advanced degree is a plus.
Python for data science and SQL.
Exploratory data analysis, statistical analysis, and predictive modeling.
Machine learning fundamentals and applied fluency with LLMs, including RAG patterns, prompt engineering, and core LLM concepts.
Model deployment via APIs or batch pipelines, with basic monitoring for performance and drift.
Git-based version control and working familiarity with CI/CD for analytical code.
Data visualization tools such as Power BI or Tableau.

Nice To Haves

Familiarity with JavaScript or React for collaboration with front-end developers.
Experience with distributed frameworks such as Spark and containerization tools such as Docker.
Exposure to cloud ML platforms is helpful but not heavily weighted.
Experience deploying in regulated, secure, or highly compliant environments.
Prior consulting or client-facing delivery experience.
An advanced degree is a plus.
Active or prior clearance is a strong plus.

Responsibilities

Translate ambiguous business questions into a clear analytical approach and select the right technique for the problem.
Conduct exploratory data analysis to understand data quality, structure, and opportunity before modeling begins.
Design, build, and evaluate analytical and machine learning solutions, including traditional ML and LLM-based approaches, using rigorous metrics and testing.
Build and assess production-grade retrieval-augmented generation pipelines when an LLM-based approach is the right fit.
Deploy models through APIs or batch pipelines, write tests, and establish basic monitoring for performance and drift.
Collaborate closely with the dedicated data architecture team on pipeline design, data quality, and schema decisions.
Apply MLOps fundamentals and CI/CD discipline to models and analytical code.
Document assumptions and communicate results clearly to both technical and business audiences.
Lead solution definition directly with client stakeholders and mentor junior technical staff.
Use modern AI and GenAI tools as a practical part of your day-to-day workflow.

Benefits

Generous retirement and pension savings contributions.
Comprehensive medical insurance for employees and immediate family.
Gym membership discounts.
Non-partner equity-based awards for consulting managers and above.
Structured and on-the-job learning and development opportunities.
Personalized opportunities including talent mobility, flexible work programs, and externships to help you chart a unique career journey.
paid time off
401(k) match
profit sharing
medical, dental, and vision coverage
healthcare concierge
backup child/adult care
annual employer HSA contribution
home office stipend
subsidized Gympass
annual wellness programming
leaves of absence when needed to support employees’ physical, mental, and emotional well-being.