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About This Role
Data Science Consultant
Location
- Washington, DC Area (Hybrid)
Summit is a specialized analytics firm that uses economics, statistics, and analytics to solve complex challenges for federal, state, and private\-sector clients. Our work in federal infrastructure finance, transaction advisory services, evidence\-based program evaluation, modernization, and enforcement and litigation analytics helps make government effective for the public good.
We are committed to fostering a collaborative workplace built on diverse perspectives, continuous learning, and trust. At Summit, you'll find an environment that empowers you to grow, contribute meaningfully, and do your best work.
We are seeking a Data Science Consultant / Senior Data Science Consultant to join our team, which partners with clients to transform complex data into actionable insights through modern analytics, machine learning, and data\-driven decision support. Successful candidates will be comfortable moving between exploratory data analysis, machine learning, cloud\-based ETL development, and modern software development practices to deliver production\-ready analytical solutions.
Primary Duties
- Conduct exploratory data analysis to identify trends, patterns, anomalies, and actionable insights from complex datasets.
- Ability to translate ambiguous business or policy questions into structured analytical approaches and actionable insights.
- Develop compelling data visualizations and communicate analytical findings through presentations, dashboards, and written summaries for technical and nontechnical audiences.
- Apply statistical methods and machine learning techniques to solve business and policy problems, including segmentation, predictive modeling, and pattern detection.
- Rapidly investigate emerging business questions through iterative data analysis and hypothesis testing.
- Design scalable, repeatable analytics and data engineering workflows that support production\-ready analytical solutions.
- Mentor junior staff in data science and software development best practices.
- Contribute to technical responses for data science proposals.
Minimum Technical Requirements
- S. citizenship and ability to obtain a Public Trust clearance
- 5\+ years total experience in data science including data preprocessing, data visualization, statistical techniques and concepts, and data management.
- Experience using AI\-assisted development tools (e.g., GitHub Copilot) in conjunction with strong Python and/or R programming skills to accelerate data analysis, analytical workflow development, and visualization while validating results.
- 4\+ years of experience programming in Python and/or R, including the ability to review, modify, and validate code.
- 2\+ years of consulting experience with project management skills.
- Experience developing analytical solutions in cloud environments (e.g., AWS or Azure), including data storage, ETL pipelines, and cloud\-based analytics services.
- Experience using version control (e.g., Git).
- Bachelor's degree.
Preferred Skills
- SQL
- Experience with cloud\-native\-data platforms and services (e.g., Amazon S3, AWS Glue, Athena, Redshift, Azure Data Factory, etc.)
- Experience deploying and maintaining code using Git\-based workflows and CI/CD (e.g. GitHub Actions, GitLab pipelines, Azure DevOps)
- Experience with machine learning techniques such as clustering, classification, or anomaly detection
- Experience supporting data\-driven decision making for government, regulatory, or policy organizations
- Tableau
Compensation and Benefits
Full\-Time Salary Range: $90,000\-110,000
This range reflects our market\-based pay structure. Individual compensation is determined by factors including business needs, local market conditions, internal equity, and candidate qualifications (skills, education, experience).
We support our employees' health, financial well\-being, and work\-life balance. Full\-time employees are eligible for:
- Medical, dental, and vision insurance
- Health Savings Account (HSA) options
- 401(k) with 4% employer match
- Disability, life, and accident insurance
- Holidays and generous paid time off, including to vote and volunteer
- Paid parental, military, bereavement, and applicable federal/state sick leave
Our Hiring Process
If selected to move forward, you will:
- Participate in an initial behavioral phone screen with our recruiting team.
- Meet with staff members for a technical and collaborative interview.
- Complete a background check (if selected for further consideration).
Work Environment
Our office is in the heart of Washington, DC's Chinatown neighborhood, with convenient access to public transportation. We operate in a hybrid model and provide an integration partner to support your onboarding and professional development.
This position may require 2\-3 days per week on site with our client in Washington, DC.
How to Apply
To ensure your application is reviewed, submit your résumé directly through our website at: www.summitllc.us/careers
Please do not email résumés.
Accessibility and Equal Opportunity
Summit is committed to accessibility for all candidates. If you are a qualified individual with a disability or a disabled veteran and require a reasonable accommodation or are unable to use or access our careers website, please contact us at peopleops@summitllc.us.
Summit is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, age, religion, sex, sexual orientation, gender identity or expression, national origin, disability, protected veteran status, or any other characteristic protected under federal, state, or local law, where applicable. Those with criminal histories will be considered in accordance with applicable state and local laws. Equal Employment Opportunity (EEO) is the law. Click here to view information on your protections under federal law from discrimination.
Salary Context
This $90K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Summit Consulting, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($100K) sits 54% below the category median. Disclosed range: $90K to $110K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Summit Consulting AI Hiring
Summit Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $110K - $110K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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