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About This Role
Dexian Government Solutions is recruiting for a Data Scientist to support our contract at the USPSOIG in Arlington, Virginia.
Position Description
- Works independently and within teams to use the necessary data extraction, manipulation, and aggregation techniques to prepare, clean, normalize, and validate data to complete varied projects and tasks.
- Researches, designs, and develops visualization solutions using a range of methods which support investigative and audit products.
- Designs experiments, tests hypotheses, and builds scalable models using data science and artificial intelligence (e.g. machine learning) methods.
- Designs, develops, and adapts mathematical, statistical, econometric, and other analytical solutions for audit, investigation, research, and support functions.
- Leads artificial intelligence activities such as natural language processing, predictive analytics, and machine learning model development, training, evaluation, testing, refinement, deployment, and maintenance.
- Translates complex technical findings into an easily understood narrative. Prepares comprehensive documentation for requirements, test plans, user manuals, technical diagrams, and training materials.
- Develops project communications and maintains effective working relationships between project teams, stakeholders, and management.
- Provides advice on issues affecting projects, such as data access, quality, storage, and other related needs.
- Contributes to and presents training and conference materials to large audiences.
- Independently performs comprehensive and efficient data collection and analysis of a variety of data sources to develop trends, descriptive statistics, or other insights.
- Uses expert level knowledge to identify and develop sources of information from structured and unstructured data, criminal intelligence databases, public information sources, internal Postal Service databases, reference manuals, and audit and law enforcement reports.
- Independently researches, extracts, evaluates, interprets, and visualizes data and information as actionable intelligence for auditors and investigators to detect, prevent, and respond to fraud, waste, and abuse.
- Uses relational databases, data lakes, data lakehouses, and other data environments to create a variety of analytic products such as business intelligence tools, summary tables, comparison graphs, or temporal, association, and link analysis charts.
- Interacts with other agencies and builds relationships with peers to share information and learn the latest developments in analytical tools and techniques to effectively support the OIG with mission related work.
- Develops substantial knowledge of database applications and environments and shares expertise with coworkers in support of agency goals and objectives.
- Expert proficiency in common data science tools, including scripted languages (such as Python, R, and JavaScript), Integrated Development Environment and analytics platforms, open\-source solutions, commercial off\-the\- shelf tools and hardware\-based capabilities to support the data analytic development process and creating models, dashboards, and reports.
- Knowledge and experience using advanced analytic techniques such as machine learning, natural language processing, robotic process automation (RPA), artificial intelligence, text and/or data mining, and statistical and mathematical methods.
- Knowledge and experience using business intelligence applications and reporting technologies/methodologies including Data Analytics Expressions (DAX), data Mash\-up(M), and Microsoft Power Platform (e.g., Power BI, Power Apps, Power Automate, etc.).
- Knowledge of AWS or Azure Services, including Databricks, Data Factory, Data Lakehouses, and Data Lake.
- Knowledge of Extraction, Transformation, and Load (ETL) strategies, pattern recognition, and application of analytical tools.
- Coordinate with staff and customers to identify business and technical requirements.
- Produce written documentation and artifacts for all work completed, including the translation of user requirements into technical designs.
- Assist the agency in the development of programming and visualization solutions.
- Troubleshoot and provide support on existing projects or application efforts.
- Understand the concepts supporting relational databases, data warehousing, data governance, data access, data quality and related areas.
- Knowledge of ODBC connection strings, and other external data source connection protocols.
- Engineer data analytic solutions, including prototyping, proof of concept, and full implementation.
- Evaluate, assess, document, and test data security and continuity of operations for systems and programs.
- Ensure compatibility between equipment and software, analyze operational/systems requirements, support design reviews, and present technical briefings.
Position Requirements:
- Proficiency in common data science tools and programming and scripting languages such as SQL, Python, R, and JavaScript with a proven ability to create solutions in complex environments, including the use of programming languages to create datasets, visualizations, and interactive reports in various business intelligence applications.
- Skill applying analytical techniques, methods, and processes to business problems demonstrated through a history of accepted modeling and analyses that resulted in meaningful business impact. These include working with unstructured or structured data and converting those data sets using a variety of analyses such as optimization, simulation, classical and spatial statistics, and/or programming languages.
- Skill using advanced analytic techniques such as machine learning, natural language processing, robotic process automation (RPA), artificial intelligence, text and/or data mining, and statistical and mathematical methods.
- Strong writing and documentation skills to capture collection of source data, methodology from business rules, and visualization deployment from a myriad of sources and interactions with various stakeholders.
- Perform analysis of data for Extraction, Transformation, and Load (ETL) strategies, pattern recognition, and application of analytical tools.
- Review, analyze, and modify existing products including coding, debugging, testing, and documenting.
- Provide guidance to coworkers on business and technical issues affecting projects, such as data access, data quality, storage capacity, and analytic tools and software.
- Assist with training and conference development which may include presentations to large audiences.
- Facilitate between business owners and end\-users who need to communicate with database administrators and traditional IT support staff.
- Ensure that quality/security guidelines are followed.
- Strong relational database and querying languages experience.
- Strong verbal and written communication skills.
- Must be able to work effectively in a team environment.
- Understand and follow a software development lifecycle (analysis, design, development, coding, testing, debugging, and documenting).
Desired Requirements:
- Specialized experience working with programming languages (e.g., Python), business intelligence tools (e.g., Power BI), and analytics platforms (e.g., Databricks).
- Knowledge and experience in the law enforcement and/or audit industry
- Knowledge and experience using cloud computing platforms such as Azure
- Knowledge and experience with relational databases and structured query language (SQL)
- Certifications: Professional Certification(s) in a related field of data science and/or data analytics disciplines preferred.
Required Experience:
- Degree in Computer Science, Information Technology, Data Analytics, or related field.
- 5\+ years' experience and skill writing coding languages (such as SQL, Python, R, and JavaScript).
- 3\+ years' experience working with projects involving machine learning, natural language processing, robotic process automation (RPA), artificial intelligence, text and/or data mining, as well as statistical and mathematical methods.
- At least 6 months' experience working with AWS or Azure services such as Databricks, Data Factory, and Data Lake.
Company Description
Dexian Government Solutions is an award\-winning, ISO 9001:2015 certified, business and GSA contract holder providing diversified Information Technology services to both Civilian and Defense agencies. Services include Software Development, Systems Integration, Data Management, Project Management, Operations \& Maintenance, Cybersecurity, and Training and Audio/Visual (AV) Solutions. Dexian Government Solutions has received several recognitions, including rankings on "Top 50 Companies to Watch", Washington Technology's Annual "FAST 50", and Inc. 500's List of "Fastest Growing Private Companies". The Dexian Government Solutions team is comprised of individuals who are dedicated to the success and sustainability of our customers and their missions. Our combination of technical expertise, big business experience, and small business agility allows us to promptly provide our customers with exceptional IT and engineering solutions.
Benefits
Our robust benefits package includes Open Paid Time Off, 11 Federal Paid Holidays \& 5 Paid Sick Days, Company\-paid Life/AD\&D, Company\-paid Short Term and Long\-Term Disability, Health Insurance with Company Contribution, 401k Plan with Company Match, Employee Recognition Program, opportunity for Employee Referral Bonus, opportunity for annual Performance Bonus and much more!
EEO Statement
Dexian Government Solutions is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All employment is decided based on qualifications, merit, and business need.
All applicants will be considered for employment without attention to race, religion, color, national origin, ancestry, physical or mental disability, medical condition, pregnancy (including childbirth, lactation and related medical conditions), marital status, genetic information (including characteristics and testing), gender, sexual orientation, gender identity or expression, military and veteran status, or any other status protected under federal, state, or local law in the locations where we operate.
If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact Human Resources. The Company invites any applicant and/or employee to review the Company's written Affirmative Action Plan. This plan is available for inspection upon request.
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Salary Context
This $140K-$145K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At LinTech Global, 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. This role's midpoint ($142K) sits 28% below the category median. Disclosed range: $140K to $145K.
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.
LinTech Global, Inc. AI Hiring
LinTech Global, Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in Arlington, VA, US. Compensation range: $145K - $145K.
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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