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About This Role
Data Scientist
Location: Atlanta, GA Hybrid
Employment Type: Full\-Time
About Us: Datavault AI, along with its event\-technology subsidiary Event Citadel (formerly CompuSystems), operates across a diverse portfolio of technology and service divisions.
Datavault AI Inc. delivers high\-performance computing software, Web 3\.0 data\-management solutions, and advanced audio technologies to a broad range of industries.
Event Citadel (formerly CompuSystems), founded in 1976, is a trusted provider of end\-to\-end event technology solutions, offering registration, ticketing, lead retrieval, and attendee\-engagement services for events of all sizes across trade, association, corporate, and government markets.
Job Description:
We’re looking for a Data Scientist to drive measurable improvement of our AI systems — including a multi\-agent LLM pipeline that profiles, classifies, and values customer data assets, and a classification service that builds our reference dataset from public sources. You’ll own the evaluation strategy, ground\-truth corpus design, and statistical rigor that turns “the agent feels better” into “the agent is measurably 18% more accurate at industry classification on our latest corpus.” This is a hands\-on, high\-ownership role where you’ll be the technical authority on what “good” looks like for our AI outputs.
Key Responsibilities:
- Design, build, and maintain evaluation frameworks for our multi\-agent LLM pipelines covering classification, PII detection, valuation, retrieval, segmentation, and synthesis — with regression\-detection rigor.
- Curate, expand, and version synthetic and real\-world test corpora that exercise our AI pipelines end\-to\-end across 20\+ industry verticals.
- Quantify model performance: precision, recall, calibration, inter\-rater agreement against human\-verified ground truth, drift detection across releases.
- Partner with engineering to design prompt experiments, agent variants, and structured\-output schema iterations; report results with statistical confidence intervals — not anecdotes.
- Improve vector\-search comparable retrieval: embedding model selection, retrieval evaluation (recall@k, MRR), taxonomy refinement, classification accuracy uplift.
- Evaluate prompt strategies, tool\-use patterns, and routing logic; recommend model\-tier choices backed by cost/accuracy data.
- Profile production traces to identify failure modes (hallucinated outputs, mis\-classifications, missed PII), then design experiments to fix them.
- Work cross\-functionally with engineering, product, and domain experts to translate fuzzy product goals (“the analysis should feel insightful”) into quantitative success metrics.
- Communicate findings through written reports, dashboards, and decision memos that executive leadership can act on.
Qualifications:
- Bachelor’s degree in Computer Science, Statistics, Data Science, Machine Learning, or related quantitative discipline, or equivalent professional experience. Master’s or PhD preferred.
- 3\+ years of professional data science, ML engineering, or AI evaluation experience shipping models or AI systems to production.
- Strong Python skills (Pandas, NumPy, scikit\-learn, PyTorch or TensorFlow), with comfort writing production\-quality code that engineers will run in CI.
- Proven experience evaluating LLM\-based systems: prompt experimentation, structured\-output validation, hallucination detection, retrieval evaluation, judge\-LLM patterns.
- Solid grasp of classical statistics: hypothesis testing, confidence intervals, sample\-size calculation, power analysis, calibration.
- SQL proficiency for ad\-hoc analysis on PostgreSQL; comfortable with embedded analytical databases for offline evaluation.
- Experience with vector databases and embedding models.
- Ability to translate business goals into measurable evaluation criteria, and willingness to push back when a “metric” doesn’t measure what stakeholders thin
What We Offer:
- Competitive salary and benefits package.
- A fast\-paced, high\-impact work environment.
- Opportunity to work closely with executive leadership.
- The chance to work with cutting\-edge technologies and make a significant impact.
- A culture of innovation, ownership, and growth.
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 Datavault AI, 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.
Datavault AI AI Hiring
Datavault AI has 1 open AI role right now. They're hiring across Data Scientist. Based in Atlanta, GA, US.
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
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