Interested in this Data Scientist role at GRVTY?
Apply Now →Skills & Technologies
About This Role
What Impact You'll Have
GRVTY is a member of 100% of the winning teams for the largest technology program in the Intel Community. We've been supporting this customer on many different sub\-projects of this program since our founding in 2013\. We've grown on this effort by providing the customer with Engineers who have done exceptional work, and we've retained our staff by paying very strong salaries, and working hard to ensure each Engineer is doing work that aligns with their career interest. Work on this program takes place in McLean, VA and in various field offices throughout Northern VA (we cannot support remote work), and requires a TS/SCI \+ Polygraph clearance (acceptable to this customer).
GRVTY is seeking a Data Scientist with a TS/SCI \+ Poly clearance (acceptable to this customer) to join one of our top projects in McLean, VA. The Sponsor provides data\-driven business analysis to support senior organizational leaders. As such, the Data Scientist will provide support specializing in natural language processing (NLP) and associated data preparation to help identify challenges and opportunities for the Sponsor’s customers. The Data Scientist must also be experienced in areas of SQL and Python to be able to transform the Sponsor’s structured and unstructured data into clear and supported analytic insights to help customers with decision making related to production, resources and personnel. The work may be performed independently or within a team environment.
What You'll be Owning
- Working closely with the Sponsor’s data scientists and technical team to implement requirements; however, the Sponsor’s GTM will manage the priorities
- Conducting sophisticated analysis using deployed tools and natural language processing
- Analyzing large amounts of raw data, including text data, to provide business insights
- Pre\-processing and/or cleaning structured and unstructured Sponsor data, including text data
- Designing and implementing advanced ETL code and table configurations for complex data sets
- Using Structured Query Language (SQL) in Sponsor’s Oracle database to develop and organize relevant information with supporting analytics
- Independently, or with a team, authoring analytic publications and produce ad\-hoc reports to include data visualizations using the Sponsor’s templates
- Staying current with the Sponsor’s enterprise metadata collection tools
- Implementing the Sponsor’s existing coordination process
- Providing technical education and subject matter expertise in NLP to support Sponsor’s initiatives to staff on an ad\-hoc basis
What You Must Have
- Active TS/SCI with Polygraph Clearance (acceptable to this customer)
- Demonstrated professional or academic experience performing NLP tasks, including selecting the best Python libraries for a given task, choosing appropriate pre\-processing actions, performing analysis, and assessing model performance
- Demonstrated professional or academic experience using Python NLP packages such as Spacy, Gensim, or NLTK to analyze or process collections of documents
- Demonstrated professional or academic experience with deep learning frameworks such as PyTorch, Tensorflow, or Keras
- Demonstrated professional or academic experience with the HuggingFace Transformers library and hub
- Demonstrated experience creating machine learning models that conduct text classification and topic modeling in Python using standard machine learning (Scikit\-learn) or deep learning models
- Demonstrated academic or professional experience using encoder\-decoder and generative language models to perform NLP tasks
- Demonstrated academic or professional experience communicating methodological choices and model results
- Demonstrated professional or academic experience and proficiency with SQL to include using common table expressions, set operations, aggregated functions and nested subqueries
- Demonstrated professional or academic experience with version control systems such as Github and Jenkins
- Demonstrated experience leveraging GPUs for accelerated computing
- Develop practical approaches for measuring performance
- Assist in developing types of measure, the collection of data, analyzing the data, and presenting that data to senior leadership
- Conduct advanced statistical analysis on personnel, intelligence, and performance metrics
- Assist in selection or development of appropriate methodology to conduct research
- Analyze information and provide research findings in a manner that is easily grasped by the customer and consumers
What Would be Nice to Have
- Demonstrated experience writing Python scripts that pull data from web\-based APIs and relational databases
- Demonstrated experience with cloud computing development and architecture
- Demonstrated experience with front\-end web development frameworks such as Flask
- Demonstrated experience developing applications for semantic search
- Demonstrated experience tuning LLMs on custom data sets and applying results to specific use cases
- Demonstrated professional or academic experience and proficiency with Tableau to produce visualizations and dashboards
\#LI\-BPJ
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 GRVTY, 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.
GRVTY AI Hiring
GRVTY has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, Data Scientist. Positions span Cambridge, MA, US, Honolulu, HI, US, McLean, VA, US. Compensation range: $220K - $225K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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.