Description

About the Role

As a Data Scientist, you're building genuine hands-on data science skill across the full CRISP-DM lifecycle - business understanding, data understanding, feature engineering, and statistical modeling. Data science is the job here, not a data-analyst-plus-business-analyst-plus-QA blend. You'll work with strong SQL and Python, applying real statistical modeling and exploratory data analysis to real business problems - school or project-based ML/EDA experience is a perfectly acceptable starting point. Around that core, you'll be capable of client-facing discussions and translating requirements into modeling tasks, and your delivery proof point is data/model validation rigor - UAT test cases, model development and validation - not front-end QA. You'll work closely with our dedicated data architecture team, and with our senior Data Scientist / Product Engineer on more complex problems, growing toward that role over time.

What you'll do

• Work hands-on across the CRISP-DM lifecycle: business understanding, data understanding, feature engineering, and statistical modeling

• Apply strong SQL and Python to real data problems - exploratory data analysis, data preparation, and model building

• Build and evaluate statistical and machine learning models under guidance from senior technical staff

• Validate models and data rigorously: write UAT test cases and own data/model validation and automated validation scripts - using tools like pytest and RTF for model/data validation automation - rather than front-end QA

• Participate in client-facing discussions, translating business requirements into concrete modeling tasks

• Collaborate with our dedicated data architecture team on data understanding and feature engineering, without owning deep pipeline architecture

• Apply modern AI/GenAI tools in your data workflows - coding assistants, LLM-assisted EDA, and similar - as a practical, everyday skill

• Document your work clearly enough for both technical and non-technical audiences to follow

Who you are

After nearly 100 years, we know this business is fundamentally about making connections - between facts, technologies, and above all, people. We look for collaborative, inquisitive problem-solvers who don't accept the first thing in front of them, who are always unapologetically themselves, and who take real ownership of the rigor behind their models and data.

In addition, we look for individuals with the following experience or qualities:

• Roughly 0-3 years of experience in data science, analytics, or a related hands-on modeling role - school or project-based ML/EDA experience is genuinely acceptable in place of professional tenure

• Strong, demonstrated SQL and Python skills, with real exposure to statistical modeling and exploratory data analysis

• Comfortable across the CRISP-DM lifecycle - business understanding through feature engineering and modeling - rather than narrowly focused on one stage

• A genuine quality mindset for model and data validation - attention to detail and a habit of double-checking rather than assuming

• Capable of client-facing discussions, translating business questions into modeling tasks

• Real business acumen: even in a technical role, comfortable in front of a stakeholder. Forge pods are small, and everyone contributes to the client relationship

• Industry-vertical background (aerospace & defense, large-scale/heavy construction, manufacturing, supply chain, or S&OP) is helpful but not required at this level - it becomes a requirement as you grow toward the senior Data Scientist role

• Comfortable operating in fast-paced, iterative, client-facing environments

• Strong written and verbal communication skills

• Comfortable using modern AI/GenAI tools day to day, or eager to build that fluency quickly

• English fluency

Required qualifications

• Ability to obtain, or current possession of, a U.S. Secret security clearance is required; an active or prior clearance is a strong plus

• Bachelor's degree in Computer Science , Data Science, Statistics, Mathematics, Engineering, or a related field - or equivalent demonstrated experience; school or project-based ML/EDA experience is acceptable in place of professional experience

Technical skills and tools

Core (expected):

• CRISP-DM lifecycle fundamentals (business understanding, data understanding, feature engineering)

• Statistical modeling and exploratory data analysis

• Strong SQL and Python

• Basic data pipeline and relational data-modeling literacy

Preferred / a plus:

• Pytest and RTF in particular, for model/data validation automation

• Exposure to ETL tooling or a cloud data platform (AWS, Azure, or Google Cloud)

• Git-based version control familiarity

• Basic exposure to LLM-assisted EDA or GenAI-assisted analysis workflows

Location

• Washington, D.C. or Arlington preferred.
• Boston and other East Coast hubs will be considered.
• Must be able to work closely with the team and travel to client delivery sites as required.
• U.S.-based only.

What we can offer you

Every day, our people work to be the difference for our clients, our communities, and our colleagues. Helping them make an impact, they are sustained by a competitive remuneration package plus comprehensive benefits and perks, including but not limited to:

• 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.

Compensation Range: $90k-$130k: It is important to note that at Kearney, it is not typical for an individual to be hired at the top of the range for their role. Individual salaries within each range are determined through a wide variety of factors, including but not limited to education, experience, knowledge, and skills. Kearney reviews compensation regularly and may adjust base salaries to reflect market competitiveness. In addition to salary, individuals may be eligible for a discretionary performance bonus.Our full suite of benefits includes paid time off, 401(k) match and 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, and leaves of absence when needed to support employees' physical, mental, and emotional well-being.

Read more about our benefits and careers at Kearney Benefits and Kearney Careers .