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About This Role
Overview:
We are seeking aData Scientist to design, build, and deliver predictive analytics, machine learning models, and data\-driven insights that support mission and business outcomes. This role requires strong analytical and modeling skills, hands\-on experience with ML frameworks, and the ability to collaborate across engineering, data, and product teams. The Data Scientist will contribute to the full lifecycle of model development, from data exploration and feature engineering to model training, evaluation, and deployment, while supporting the articulation of insights and recommendations to technical and non\-technical stakeholders.
Contributions:
- Conduct exploratory data analysis (EDA) to uncover trends, identify data quality issues, and determine modeling opportunities.
- Develop machine learning models using frameworks such as scikit\-learn, XGBoost, PyTorch, TensorFlow, or similar libraries.
- Perform feature engineering, dataset preparation, and model optimization to improve predictive accuracy and operational performance.
- Evaluate models using appropriate statistical methods and performance metrics, documenting findings and informing iterative improvements.
- Work with Data Engineers to understand and enhance data pipelines, ensuring model\-ready datasets are accurate, complete, and consistent.
- Collaborate with AI Developers and LLMOps/MLOps Engineers to integrate ML models into production environments or decision\-support applications.
- Build visualizations, dashboards, data stories, and executive\-friendly summaries to communicate insights clearly to stakeholders.
- Support human\-centered design processes by translating user needs into modeling requirements and refining model outputs based on feedback.
- Contribute to reproducibility and auditability by maintaining well\-organized code, notebooks, documentation, and experiment histories.
- Stay current with modern data science methodologies, ML techniques, data processing tools, and cloud\-enabled analytics workflows.
- You will contribute to the growth of our AI \& Data Exploitation Practice!
Qualifications:
- Ability to hold a position of public trust with the U.S. government.
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Analytics, Engineering, Economics, or a related field.
- 4\+ years of experience building and evaluating machine learning models or performing advanced analytics.
- Proficiency in Python and core data science libraries (Pandas, NumPy, scikit\-learn, Matplotlib/Seaborn).
- Hands\-on experience with at least one modern ML framework (e.g., PyTorch, TensorFlow, XGBoost).
- Experience writing efficient SQL queries and working with structured or semi\-structured data in cloud or database environments.
- Familiarity with cloud data and ML platforms such as AWS, Azure, GCP, or Databricks.
- Strong understanding of statistics, probability, experimental design, and model validation techniques.
- Ability to communicate technical concepts clearly to stakeholders and collaborate across multidisciplinary teams.
- Experience with dashboarding tools (Tableau, Power BI) or Python visualization libraries is a plus.
- Curiosity and adaptability, with a drive to stay current in rapidly evolving data and ML practices.
- Relevant certifications (helpful but not required): Databricks Data Scientist Associate, AWS ML Specialty, Azure Data Scientist Associate, Google Professional Data Engineer.
About steampunk:
Steampunk relies on several factors to determine salary, including but not limited to geographic location, contractual requirements, education, knowledge, skills, competencies, and experience. The projected compensation range for this position is $125,000 to $160,000\. The estimate displayed represents a typical annual salary range for this position. Annual salary is just one aspect of Steampunk’s total compensation package for employees. Learn more about additional Steampunk benefits here.
Identity Statement
As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
Steampunk is a Change Agent in the Federal contracting industry, bringing new thinking to clients in the Homeland, Federal Civilian, Health and DoD sectors. Through our Human\-Centered delivery methodology, we are fundamentally changing the expectations our Federal clients have for true shared accountability in solving their toughest mission challenges. As an employee owned company, we focus on investing in our employees to enable them to do the greatest work of their careers – and rewarding them for outstanding contributions to our growth. If you want to learn more about our story, visit http://www.steampunk.com.
Salary Context
This $125K-$160K range is below the median for Data Scientist roles in our dataset (median: $162K across 211 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 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Steampunk, 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 $200,000 based on 697 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($142K) sits 29% below the category median. Disclosed range: $125K to $160K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Steampunk AI Hiring
Steampunk has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span McLean, VA, US, US. Compensation range: $160K - $190K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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