The Role

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.
Questions You Might Answer

• Did a higher NER or entity-resolution score actually reduce sensitive misses across the messages, documents, tables, and providers that matter?
• Is a new model finding more sensitive information, or simply removing more of the useful structure our customers need?
• Can we trust a golden dataset, a human review process, or an LLM judge enough to use it for a release decision?
• Which customer, modality, entity, language, or format slices are hidden by a strong aggregate result?
• Where did a quality loss enter between source data, processing, de-identification, review, and delivery?
• What is the smallest credible experiment that would tell us whether to ship, revise, or stop a change?

What You'll Do

• 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 Success Looks Like

• The team has a decision-grade baseline for a priority Clean Data quality claim and trusts it enough to use in model, pipeline, release, and delivery decisions
• Improvements are judged by the slices and failure costs that matter, not only by an aggregate benchmark
• The company can distinguish a true gain from label noise, sample bias, leakage, evaluator error, or a shifted workload
• Changes that improve one stage cannot hide escaped defects, over-redaction, utility loss, or review burden somewhere else
• At least one consequential decision changes because the evidence reveals a risk, tradeoff, or opportunity that was previously unclear
• Evaluation becomes faster and more repeatable without sacrificing independence or rigor
• Quality claims communicate uncertainty honestly and remain understandable to engineers, customers, and risk owners

You Might Thrive Here If

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

This Role May Not Be for You If

• You want to optimize models as your primary job rather than determine whether changes actually improve delivered data
• You prefer descriptive dashboards that stop short of changing a decision
• You treat labels, benchmarks, or model-based judges as ground truth without investigating how they fail
• You need a perfectly defined dataset and research plan before you can make progress
• You are uncomfortable disagreeing with a technically strong team when the evidence does not support its conclusion
• You do not want AI tools to be part of your daily scientific and technical workflow

Bonus

• 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