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About This Role
Description: About Us:
eSimplicity is a modern digital services company that partners with government agencies to improve the lives and protect the well-being of all Americans, from veterans and service members to children, families, and seniors. Our engineers, designers, and strategists cut through complexity to create intuitive products and services that equip federal agencies with solutions to courageously transform today for a better tomorrow.
Purpose and Scope:
We are seeking a Data Scientist to join our Data & AI team. This role will apply advanced analytics, machine learning, and artificial intelligence to large and complex datasets to identify patterns, develop predictive insights, and support high-impact decision making. The ideal candidate is a hands-on data scientist who is comfortable working directly with stakeholders, developing analytical approaches from ambiguous problems, and communicating complex findings clearly.
This position is contingent upon contract award.
Responsibilities:
- Design, develop, test, and maintain advanced statistical, machine learning, and AI models.
- Develop supervised and unsupervised models including regression, classification, clustering, anomaly detection, and related approaches.
- Analyze large structured and unstructured datasets using Python, Pandas, and SQL.
- Support development and implementation of semantic layer architectures.
- Develop analytical methods, indicators, and models to identify patterns, anomalies, relationships, and potential risk.
- Work directly with investigative and business stakeholders to translate questions into analytical approaches and actionable findings.
- Develop and scale natural language processing solutions, including techniques such as semantic analysis, OCR, and large language models.
- Develop dashboards, visualizations, and analytical products to communicate methods, findings, and recommendations.
- Document analytical methodologies, model testing, assumptions, and results.
- Collaborate with data engineers and technical teams to support analytics and machine learning in cloud environments.
Competencies:
- Applied Data Science: Translates complex business or investigative questions into practical analytical approaches and models.
- Technical Depth: Demonstrates strong hands-on capability across Python, SQL, statistical modeling, machine learning, and modern AI techniques.
- Problem Solving: Works effectively with incomplete or complex data and adapts analytical approaches as requirements evolve.
- Communication: Clearly explains analytical methods and findings to technical and non-technical stakeholders.
- Collaboration: Works effectively with stakeholders, data engineers, and other technical team members to deliver usable analytical solutions.
Requirements:
- All candidates must pass public trust clearance through the U.S. Federal Government. This requires candidates to either be U.S. citizens or pass clearance through the Foreign National Government System which will require that candidates have lived within the United States for at least 3 out of the previous 5 years, have a valid and non-expired passport from their country of birth and appropriate VISA/work permit documentation. 10+ years working in AI/ML, DevOps, development, IT consulting or other technology-related fields
- Master’s degree, Ph.D., or equivalent advanced degree in Data Science, Machine Learning, Computer Science, Mathematics, or a related field; OR
- 10+ years of applied experience in Data Science, Machine Learning, Computer Science, Mathematics, or a related field
- Hands-on experience developing advanced AI, machine learning, predictive, and statistical models, including supervised and unsupervised approaches.
- Hands-on Python experience, including Pandas.
- Hands-on experience supporting semantic layer development or implementation.
- Experience working in a modern cloud environment such as Azure, AWS, or GCP.
- SQL experience, including SQL Server and PostgreSQL.
- Experience developing and scaling natural language processing solutions.
- Experience presenting analytical methods and findings to technical and non-technical stakeholders through written products, presentations, dashboards, or visualizations.
- Excellent command of written and spoken English.
Desired Qualifications:
- Ph.D. in Computer Science, Data Science, Machine Learning, Mathematics, or a related discipline.
- Experience leading data architecture efforts to support machine learning implementations.
- Experience supporting fraud, waste, abuse, financial-crime, or program-integrity analytics.
- Experience with Microsoft Azure, including Azure Machine Learning, Synapse Analytics, AI Foundry, or AI Search.
- Experience developing fraud indicators, risk-scoring approaches, anomaly-detection models, or investigative leads.
- Experience with OCR, semantic similarity, entity resolution, or analysis of large unstructured datasets.
- Experience integrating LLMs or retrieval-augmented generation solutions with enterprise data.
- Experience developing analytical dashboards or network/link visualizations.
- Cloud, AI, or machine learning certifications.
Working Environment:
eSimplicity supports a remote work environment operating within the Eastern time zone so we can work with and respond to our government clients. Expected hours are 9:00 AM to 5:00 PM Eastern unless otherwise directed by manager.
Occasional travel for training and project meetings. It is estimated to be less than 5% per year.
Benefits:
eSimplicity offers a comprehensive benefits package, including medical, dental, and vision coverage, 401(k) retirement benefits, paid time off, paid holidays, life and disability insurance, and additional wellness and employee support programs. Eligibility may vary based on employment status and applicable plan terms.
Reasonable Accommodation:
eSimplicity is committed to providing reasonable accommodations to qualified individuals with disabilities during the application and hiring process. Applicants who need assistance or an accommodation should contact Human Resources.
Equal Employment Opportunity:
eSimplicity is an Equal Opportunity Employer, including disability and protected veteran status. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran status, disability, or any other legally protected status.
Salary Context
This $160K-$175K range is above the median for Data Scientist roles in our dataset (median: $167K across 150 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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Esimplicity, 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($167K) sits 12% below the category median. Disclosed range: $160K to $175K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Esimplicity AI Hiring
Esimplicity has 1 open AI role right now. They're hiring across Data Scientist. Based in Columbia, MD, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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