Data Scientist

Sunset • New York, NY

9d

About The Position

Sunset turns sensitive internal enterprise data into de-identified datasets without destroying the structure and meaning that make the data valuable. That creates a difficult measurement problem. A system can improve aggregate F1 while missing a high-risk slice, remove more sensitive information while also destroying useful context, or pass one stage while defects escape somewhere else in the pipeline.
As Sunset's first Data Scientist focused on evaluation, you will establish how we know whether that data is actually getting better. You will build the datasets, experiments, quality measures, and feedback loops that expose hidden failures, accelerate model and pipeline improvement, and give the team confidence in what it delivers.
This is a hands-on, zero-to-one role at the intersection of data science, AI, and a real production system. You will write Python and SQL, construct evaluation corpora, study failure patterns, design comparisons, calibrate human and model-based judgments, and turn the result into a clear decision. The questions are scientifically difficult, but the output must be practical enough to change what the team builds and ships.
You will work closely with Machine Learning, Product Engineering, Data Engineering, Security, Quality, domain experts, and the team making delivery decisions. Machine Learning Engineers own changing model behavior. You own the credibility of the evidence used to decide whether a model, pipeline, or delivery change actually made the data safer or more useful.

Requirements

• At least three years of professional experience in applied science, data science, machine learning, quantitative research, or a closely related role
• Designed evaluations or experiments that changed a product, model, release, or operational decision
• Understand sampling, uncertainty, precision, recall, F1, calibration, agreement, class imbalance, distribution shift, and imperfect labels
• Can investigate messy, multi-stage data systems and determine where an apparent gain or loss actually came from
• Comfortable writing Python and SQL and building reproducible technical artifacts rather than handing requirements to an engineering team
• Can protect the independence of an evaluation while collaborating closely with the people whose work it evaluates
• Startup experience and enjoy broad ownership, changing context, and building the measurement foundation while decisions are already moving quickly
• Use AI tools fluently but do not confuse an articulate model output with valid evidence
• Communicate uncertainty and difficult findings directly, without hiding behind false precision

Nice To Haves

• Experience evaluating NER, entity resolution, information extraction, document understanding, multimodal, retrieval, or LLM systems
• Experience with privacy, de-identification, data quality, model risk, safety, or other high-trust decision systems
• Experience designing human-review, adjudication, weak-supervision, or active-learning systems
• Experience building adversarial corpora, replay systems, simulation environments, programmatic verifiers, or model-judge evaluations
• Experience connecting offline measures to escaped defects, customer outcomes, review effort, or preserved data utility
• Experience measuring quality across multi-stage batch or data pipelines

Responsibilities

• Define what high-quality and safe-to-deliver data mean across de-identification, structure preservation, semantic coherence, and customer utility
• Design representative samples and build golden, adversarial, replay, and production-like corpora with explicit provenance, labeling policy, agreement, adjudication, and versioning
• Turn ambiguous concepts such as “useful,” “clean,” or “safe” into measurable claims with known uncertainty and clear decision consequences
• Evaluate detectors, models, prompts, judges, thresholds, review workflows, and pipeline changes using comparisons that can support a real decision
• Break aggregate results into the modalities, providers, entity classes, customer contexts, languages, formats, and risk tiers that reveal consequential failures
• Connect local measures to escaped sensitive information, avoidable over-redaction, preserved data utility, review burden, rework, and delivery acceptance
• Build reproducible analysis, evaluation pipelines, and high-fidelity environments using Python, SQL, synthetic data, historical replay, seeded failures, and programmatic verifiers
• Establish holdout and evaluation practices that keep the evidence trustworthy while model and product teams iterate quickly
• Use modern AI tools deeply for analysis, corpus development, coding, review, and hypothesis generation while independently verifying their output

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed