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About This Role
### Job Description
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.
Key responsibilities
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- 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.
Who you are
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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 are comfortable operating with both engineers and senior client stakeholders and who bring genuine ownership to their work.
We want to hear from you if you are:
- An experienced data science professional with a track record of owning end\-to\-end solutions and taking them into production.
- A true generalist across business framing, exploratory data analysis, modeling, deployment, and monitoring.
- Strong in business acumen and able to explain what a model means for a decision, not just how it works.
- Fluent in at least one relevant industry vertical such as aerospace and defense, large\-scale construction, manufacturing, supply chain, or sales and operations planning.
- Comfortable with prompt engineering, RAG, and core LLM concepts such as context, evaluation, and temperature.
- Credible in data engineering conversations, especially around pipelines and data quality, even if you do not own that function directly.
- Comfortable in fast\-paced, iterative, client\-facing delivery environments.
- Strong in written and verbal communication and experienced in mentoring junior technical talent.
- Practically fluent in using modern AI and GenAI tools in your own workflow.
Required qualifications
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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.
Technical skills and tools
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Core
- 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.
Preferred
- 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.
Location
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- 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
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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: $100k\-$150k: 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.
Equal employment opportunity and non\-discrimination
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Kearney prides itself on providing a culture that allows employees to bring their best selves to work every day. Our people can feel comfortable, confident, and joyful to do great things for our firm, our colleagues, and our clients. Kearney aims to build diverse capabilities to help our clients solve their most mission critical problems. Kearney is committed to building a diverse, unbiased, and inclusive workforce. Kearney is an equal opportunity employer; we recruit, hire, train, promote, develop, and provide other conditions of employment without regard to a person’s gender identity or expression, sexual orientation, race, religion, age, national origin, disability, marital status, pregnancy status, veteran status, genetic information, or any other differences consistent with applicable laws. This includes providing reasonable accommodation for disabilities or religious beliefs and practices. Members of communities historically underrepresented in consulting are encouraged to apply.
Salary Context
This $100K-$150K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Kearney, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($125K) sits 35% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Kearney AI Hiring
Kearney has 1 open AI role right now. They're hiring across Data Scientist. Based in Washington, DC, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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