Interested in this Data Scientist role at Base-2 Solutions, LLC?
Apply Now →Skills & Technologies
About This Role
- Requisition ID: 3068
- Standard Title:
- Required Security Clearance: Top Secret/SCI
- Location: Bethesda, MD
- Work Type: On\-Site
- Shift: First
- Referral Eligibility: Eligible
- U.S. Citizenship Required? Yes
Position Summary
--------------------
Base\-2 Solutions is seeking a Data Scientist to perform research\-level data analytics at a customer site in Bethesda, MD. The role supports programs advancing research and advanced machine learning, natural language processing, and data fusion applications for mission\-focused big data analytics. The Data Scientist will work as part of a research\-focused team to advance national security objectives, analyze results, disseminate findings, and contribute to publications and presentations with actionable intelligence insights.
Essential Duties and Responsibilities
-----------------------------------------
- Work closely with a team of data scientists, software developers, network engineers, senior investigators, program managers, researchers, and data analysts to design, build, and optimize a Data Science platform.
- Analyze a variety of big data covering national security, cybersecurity, business intelligence, online social media, human behavior, and more.
- Support multiple simultaneous projects, independently and collaboratively making mission\-relevant discoveries and delivering findings to non\-technical audiences.
- Identify novel data sources to improve predictive algorithms and encourage user adoption of data analytics platforms.
- Leverage expertise in research design, exploratory analysis, quantitative methods, user interface application design, and customer outreach and engagement.
Required Qualifications
---------------------------
- B.S. Degree in a quantitative or analytical field such as Computer Science, Mathematics, Economics, Statistics, Engineering, Physics, or Computational Social Science with 2\+ years of experience; or Master's degree or equivalent graduate degree including certificate\-based advanced training courses with less than 2 years of experience.
- Experience in data science, analytics, or quantitative intelligence analysis with progressive technical development and outcomes.
- Proficiency in one or more scripting languages such as Python.
- Experience deploying data science applications using Docker, Kubernetes, and/or OpenShift.
- Experience with information retrieval and search technologies such as Elastic Stack (Elasticsearch and Kibana) and/or Structured Query Language (SQL).
- Experience working with hybrid teams of analysts, engineers, and developers to conduct research and build and deploy complex but user\-friendly algorithms and analytical platforms.
- Previous experience performing Data Science or Research in data analytics or big data that does not fit in memory.
- Track record of active learning and creative problem solving.
- Ability to analyze and assess software development or data acquisition requirements and determine cost\-effective solutions.
- Familiarity with git or other version control technologies.
- Ability to own work and complete tasks on time with little or no supervision.
Preferred Qualifications
----------------------------
- Significant data analytics experience supporting military or intelligence community customers with progressive technical development and mission\-focused outcomes.
- Experience with at least two of the following data classes: forensic media (DOMEX), open source publicly available information (PAI), measurement and signatures intelligence (MASINT), or cybersecurity and cyber forensics.
- Experience with Knowledge Graphs and related technologies such as neo4j or graph machine learning.
- Experience developing predictive algorithms, image classifiers, and object detectors.
- Familiarity with social network analysis, supply chain analysis, forensic accounting, pattern of life analysis, natural language processing, social media analysis, classification algorithms, and/or image processing.
- Experience blending analytical methodologies and leveraging existing COTS/GOTS/OS tools in unconventional ways.
- Experience with on\-premises air\-gapped environments and/or Amazon Web Services (AWS/C2S).
- Familiarity with hardware platforms such as CPUs, GPUs, and FPGAs.
Required Education and Experience Equivalency
-------------------------------------------------
- B.S. Degree in a quantitative or analytical field with 2\+ years of experience.
- Master's degree or equivalent graduate degree including certificate\-based advanced training courses with Less than 2 years of experience.
Required Certifications
---------------------------
- None specified.
Required Security Clearance
-------------------------------
- Active Top Secret/SCI
Pay \& Benefit Highlights
-----------------------------
Compensation
----------------
- Competitive fixed salary or hourly pay (based on experience, skills, location, and internal equity).
- Employee referral bonuses up to $10,000 per hired referral.
- Additional bonus opportunities for exceptional performance and contributions to business development and company growth (role\-dependent).
Health
----------
- 100% company\-paid medical premiums for employees and eligible dependents.
- Choose from multiple plan options with CareFirst, Kaiser, and UnitedHealthcare, including PPO, POS, HMO, and HSA\-compatible plans.
- 100% company\-paid dental premiums for employees and eligible dependents.
- 100% company\-paid vision premiums for employees and eligible dependents.
Income Protection
---------------------
- 100% company\-paid premiums for short\-term disability.
- 100% company\-paid premiums for long\-term disability.
- 100% company\-paid premiums for accidental death \& dismemberment (AD\&D).
- 100% company\-paid premiums for life insurance up to $200,000\.
Retirement
--------------
- 401(k) with immediate vesting: 4% company match plus a 4% non\-elective company contribution (8% total).
- 401(k) pre\-tax and Roth options.
Leave
---------
- Up to 20 days of flexible paid time off (PTO).
- 11 paid floating holidays.
Work\-Life Balance
----------------------
- Flexible work schedules, including flex time and compressed work periods (contract and project\-dependent).
NT
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Base-2 Solutions, LLC, 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 $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Base-2 Solutions, LLC AI Hiring
Base-2 Solutions, LLC has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 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).
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 3,708 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.