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About This Role
Mclean, VA
Contract To Hire
Apr 9, 2026
Data Scientist Specialist\-
Summary:
We are seeking a highly experienced \*\*Principal Gen AI Scientist\*\* with a strong focus on
\*\*Generative AI (GenAI)\*\* to lead the design and development of cutting\-edge AI Agents,
Agentic Workflows and Gen AI Applications that solve complex business problems. This
role requires advanced proficiency in Prompt Engineering, Large Language Models (LLMs),
RAG, Graph RAG, MCP, A2A, multi\-modal AI, Gen AI Patterns, Evaluation Frameworks,
Guardrails, data curation, and AWS cloud deployments. You will serve as a hands\-on Gen
AI (data) scientist and critical thought leader, working alongside full stack developers, UX
designers, product managers and data engineers to shape and implement enterprise\-grade
Gen AI solutions.
\*\*Key Responsibilities: \*\*
- Architect and implement scalable AI Agents, Agentic Workflows and GenAI applications
to address diverse and complex business use cases.
- Develop, fine\-tune, and optimize lightweight LLMs; lead the evaluation and adaptation of
models such as Claude (Anthropic), Azure OpenAI, and open\-source alternatives.
- Design and deploy Retrieval\-Augmented Generation (RAG) and Graph RAG systems using
vector databases and knowledge bases.
- Curate enterprise data using connectors integrated with AWS Bedrock's Knowledge
Base/Elastic
- Implement solutions leveraging MCP (Model Context Protocol) and A2A (Agent\-to\-Agent)
communication.
- Build and maintain Jupyter\-based notebooks using platforms like SageMaker and
MLFlow/Kubeflow on Kubernetes (EKS).
- Collaborate with cross\-functional teams of UI and microservice engineers, designers, and
data engineers to build full\-stack Gen AI experiences.
- Integrate GenAI solutions with enterprise platforms via API\-based methods and GenAI
standardized patterns.
- Establish and enforce validation procedures with Evaluation Frameworks, bias mitigation,
safety protocols, and guardrails for production\-ready deployment.
- Design \& build robust ingestion pipelines that extract, chunk, enrich, and anonymize data
from PDFs, video, and audio sources for use in LLM\-powered workflows—leveraging best
practices like semantic chunking and privacy controls
\* Orchestrate multimodal pipelines\*\* using scalable frameworks (e.g., Apache Spark,
PySpark) for automated ETL/ELT workflows appropriate for unstructured media
- Implement embeddings drives—map media content to vector representations using
embedding models, and integrate with vector stores (AWS KnowledgeBase/Elastic/Mongo
Atlas) to support RAG architectures
\*\*Required Qualifications:\*\*
- PhD in AI/Data Science
- 10\+ years of experience in AI/ML, with 3\+ years in applied GenAI or LLM\-based solutions.
- Deep expertise in prompt engineering, fine\-tuning, RAG, GraphRAG, vector databases
(e.g., AWS KnowledgeBase / Elastic), and multi\-modal models.
- Proven experience with cloud\-native AI development (AWS SageMaker, Bedrock, MLFlow
on EKS).
- Strong programming skills in Python and ML libraries (Transformers, LangChain, etc.).
- Deep understanding of Gen AI system patterns and architectural best practices,
Evaluation Frameworks
- Demonstrated ability to work in cross\-functional agile teams.
- Need Github Code Repository Link for each candidate. Please thoroughly vet the
candidates.
\*\*Preferred Qualifications: \*\*
- Published contributions or patents in AI/ML/LLM domains.
- Hands\-on experience with enterprise AI governance and ethical deployment frameworks.
- Familiarity with CI/CD practices for ML Ops and scalable inference
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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Technology Ventures, 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 $204,700 based on 441 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Technology Ventures AI Hiring
Technology Ventures has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer. Positions span McLean, VA, US, Reston, VA, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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