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About This Role
Overview:
Acuity is looking for a Data Scientist to help shape next\-generation cloud and analytics solutions that transform complex data into actionable intelligence for our clients.
Why Acuity?: Acuity is a digital strategy and technology consulting firm that serves federal agencies with critical missions. Our experts support projects in the areas of IT Modernization, Data Enablement, and Hyperautomation to help protect our national interests and keep people safe. If you have technical experience and a passion for making a difference, we might be the right fit for you. Responsibilities:
- Lead the discovery, design, and delivery of advanced analytics solutions that create measurable, data\-driven value for internal stakeholders.
- Provide strategic and technical leadership across the entire analytics lifecycle, including opportunity scoping, architecture design, model development, validation, deployment, and performance optimization.
- Build, train, and operationalize predictive, prescriptive, and descriptive models using statistical and machine learning methods to produce reliable, actionable intelligence.
- Collaborate with data engineers, analysts, and business leaders to align analytical outputs with enterprise priorities and translate complex findings into clear, high\-impact decisions.
Qualifications:
- 10\+ years of progressive experience designing, developing, and delivering advanced analytics solutions within enterprise or federal data environments.
- Demonstrated mastery in applying machine learning, statistical modeling, and data science methodologies to large\-scale, high\-dimensional, and mission\-critical data sets.
- Deep expertise in quantitative analysis, forecasting, predictive modeling, deep learning, and optimization techniques to derive actionable business and operational insights.
- Extensive hands\-on experience operating in Agile delivery environments to rapidly prototype, validate, and deploy scalable, production\-ready analytics solutions.
- Advanced proficiency in two or more programming languages (Python, R, PySpark, or SQL) and demonstrated experience with both SQL and NoSQL databases, distributed computing frameworks, and modern data platforms such as Databricks and Apache Spark.
- Prior experience leveraging AWS for machine learning and analytics workloads preferred.
Clearance Requirement:* Must be a U.S. Citizen with the ability to obtain and maintain a U.S. Government Suitability clearance.
About Acuity
At Acuity, your work matters—and so does your experience. We’re a management and technology consulting firm supporting critical federal missions, where you’ll have the opportunity to solve meaningful challenges, work alongside high\-performing teams, and make a real impact from day one. Why You’ll Love Working Here: Grow Your Career, Your Way
We invest in you with personalized development plans, mentorship, and up to $3,000 annually for training and certifications and up to $3,000 for degree seeking programs—so you can keep building the career you want. Be Part of Something Innovative
You’ll work on cutting\-edge solutions that support important government missions, in an environment that encourages new ideas and continuous improvement. Thrive in a People\-First Culture
Collaboration, respect, and support aren’t just values—they’re how we operate. Your voice is heard, your contributions are recognized, and your success is shared. Feel Valued and Rewarded
We offer competitive compensation, comprehensive benefits, and a strong focus on work\-life balance so you can perform at your best—at work and at home. Join an Award\-Winning Team
Our employees consistently rank us among the best—earning honors like *Best Places to Work* (Washington Business Journal, 9\+ years) and *Top Workplaces* (The Washington Post, 2022–2025\). Bring Your Whole Self to Work
We’re committed to building a diverse, inclusive environment where everyone feels respected, supported, and empowered to succeed. Make Your Impact
Join Acuity and be part of a team where your ideas are valued, your growth is supported, and your work drives meaningful outcomes.
Learn more: www.myacuity.com *Acuity is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.* Recruiting Scams and Fraud
Acuity Inc. has been made aware of fraudulent job postings and individuals impersonating company recruiters. These scams may include fake job offers, requests for sensitive personal information, or demands for payment.
Please note:* Acuity never asks candidates to pay for job applications, equipment, or training.
- All official communications will come from an @myacuity.com email address.
- If you are unsure about a job posting or recruiter, please verify the opportunity on our Careers page.
If you believe you have been targeted by a scam or have concerns about the authenticity of a job listing claiming to represent Acuity Inc., please contact us at [email protected]. Protecting your security and trust is important to us.
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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Acuity Inc., 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Acuity Inc. AI Hiring
Acuity Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in Reston, VA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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