Data Scientist

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., * 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.

• 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

• 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.

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.