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About This Role
Data Scientist
Position Overview
The Data Scientist will support federal government programs by applying advanced statistical analysis, data science, machine learning, and data visualization techniques to complex operational and mission-related datasets. This position will work closely with government stakeholders, program leadership, analysts, and technical teams to transform structured and unstructured data into actionable insights that support data-driven decision-making, program performance, and mission objectives.
The successful candidate will have experience working with large and complex datasets, developing analytical models, identifying trends and patterns, and communicating technical findings to both technical and non-technical stakeholders.
Key Responsibilities
- Collect, clean, integrate, and analyze large structured and unstructured datasets from multiple government and enterprise data sources.
- Perform exploratory data analysis to identify trends, patterns, relationships, anomalies, and opportunities for improved program performance.
- Develop statistical models, predictive models, machine learning algorithms, and other advanced analytical solutions to address mission and business requirements.
- Apply statistical techniques including regression analysis, classification, clustering, forecasting, hypothesis testing, and predictive analytics.
- Develop and maintain data pipelines and repeatable analytical processes to support ongoing reporting and decision-making.
- Use Python, R, SQL, or similar tools to manipulate data, perform statistical analysis, and develop analytical models.
- Design dashboards, reports, visualizations, and other data products using tools such as Power BI, Tableau, or similar business intelligence platforms.
- Translate complex analytical findings into clear recommendations and actionable insights for federal stakeholders and program leadership.
- Collaborate with subject matter experts, program managers, data engineers, developers, and government personnel to define analytical requirements and identify appropriate data solutions.
- Evaluate data quality, completeness, accuracy, and reliability and recommend improvements to data collection and governance processes.
- Develop performance metrics, key performance indicators (KPIs), and analytical methodologies to measure program effectiveness and operational performance.
- Document analytical methodologies, assumptions, data sources, models, processes, and findings in accordance with contract and government requirements.
- Prepare technical reports, briefings, presentations, and executive-level summaries for government stakeholders.
- Support ad hoc analytical requests, program evaluations, forecasting, workforce analysis, and other mission-driven data initiatives.
- Ensure data is handled in accordance with applicable federal security, privacy, records management, and information governance requirements.
- Participate in quality assurance and peer-review activities to validate analytical methodologies and results.
- Support continuous improvement initiatives by identifying opportunities to automate manual analysis and reporting processes.
Minimum Qualifications
- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Operations Research, Engineering, or a related quantitative discipline.
- 3–5+ years of professional experience in data science, advanced analytics, statistical analysis, or a related field.
- Demonstrated experience analyzing large and complex datasets.
- Proficiency with Python and/or R for data analysis and statistical modeling.
- Strong SQL skills and experience extracting and manipulating data from relational databases.
- Experience developing statistical, predictive, or machine learning models.
- Experience with data visualization and business intelligence tools such as Power BI, Tableau, or equivalent platforms.
- Knowledge of statistical methodologies and quantitative research techniques.
- Ability to translate business or mission requirements into analytical approaches and measurable outcomes.
- Strong written and verbal communication skills, including the ability to present complex technical information to non-technical stakeholders.
- Ability to develop clear technical documentation, analytical reports, and executive-level briefings.
- Ability to work effectively within multidisciplinary teams and interact directly with government clients.
Preferred Qualifications
- Master's degree or Ph.D. in Data Science, Statistics, Mathematics, Computer Science, Operations Research, Economics, Industrial-Organizational Psychology, or another quantitative discipline.
- Previous experience supporting a federal government agency or government contractor.
- Experience working with federal program, workforce, human capital, financial, operational, or performance-management data.
- Experience with cloud-based analytics environments such as AWS, Microsoft Azure, or Google Cloud Platform.
- Experience with machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or similar technologies.
- Experience with natural language processing (NLP), generative AI, or large language model applications.
- Knowledge of data governance, data quality management, metadata management, and federal data standards.
- Experience developing automated data pipelines or working with ETL/ELT processes.
- Familiarity with Agile development or federal program management environments.
- Experience supporting federal reporting, program evaluation, performance measurement, or evidence-based decision-making initiatives.
Security Requirements
- Must be eligible to obtain and maintain the level of federal background investigation, Public Trust determination, or security clearance required by the contract.
- Must comply with all applicable federal and agency-specific information security, privacy, and data-handling requirements.
Core Competencies
Data Analysis • Statistical Modeling • Machine Learning • Predictive Analytics • Python • R • SQL • Data Visualization • Power BI/Tableau • Data Quality • Data Governance • Federal Reporting • Performance Measurement • Technical Documentation • Stakeholder Engagement
Compensation- $100,000
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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Business Management Associates, 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 $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Business Management Associates AI Hiring
Business Management Associates has 1 open AI role right now. They're hiring across Data Scientist. Based in Washington, DC, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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