Staff Applied Scientist (Compliance & Operations)

Quick Facts

• Role: Staff Applied Scientist (Compliance & Operations)

Description

Drive applied AI research to streamline compliance operations by building models and systems for document understanding, identifying relevant requirements, and supporting operational decisions. You will define scientific approaches under uncertainty, investigate data, develop hypotheses, train and evaluate models, and work with engineers to deploy solutions into production. The goal is to improve answer reliability while reducing processing time, errors, and manual effort.

Responsibilities

• Partner with Operations, Product, Engineering, and compliance experts to identify valuable opportunities and define testable hypotheses and success metrics.

• Develop AI workflows for document classification, information extraction, routing, and operational decisioning; measure impact on completion time, error rates, manual review, and cost.

• Build scientific foundations for identifying relevant business requirements and answering compliance questions, including when to abstain or request human review.

• Train, fine-tune, and evaluate models for business tasks; compare approaches such as pretrained models, retrieval-augmented generation, and supervised fine-tuning.

• Create reliable training and evaluation datasets; define labeling guidance, handle gaps and imbalance, and prevent training/testing leakage.

• Establish repeatable evaluations for retrieval quality, factual accuracy, task completion, and consequential failure cases; set release criteria and rerun after material changes.

• Own scientific outcomes through production adoption; deploy, monitor, diagnose performance changes, and improve latency and cost.

• Stay current with AI advances, document experiments, and share reusable methods.

Requirements

• Track record in applied science/ML of defining problems under uncertainty and moving models from experimentation to ongoing business use.

• Strong foundations in statistics, machine learning, and experimental design; deep expertise in NLP, information retrieval, or document understanding; ability to diagnose failure causes and design experiments to improve models.

• Hands-on experience training and adapting models, including practical LLM work; understanding transformer models, embeddings, retrieval, and fine-tuning.

• Strong Python and SQL skills; experience with PyTorch (or an equivalent framework); ability to write tested, maintainable code for reproducible experiments and production collaboration.

• Experience with noisy documents, limited labels, and unevenly distributed failure cases; ability to assess data quality, build representative evaluation sets, quantify uncertainty, and account for the costs of mistakes.

• Sound judgment about AI reliability and sensitive data; ability to address unsupported outputs, prompt injection, and data exposure with traceable results and fallback behavior.

• Autonomy to identify the problem when goals or evidence conflict; prioritize experiments and make practical trade-offs; clear English communication.

• Master’s or PhD in computer science, machine learning, statistics, or a related discipline, or equivalent experience demonstrating comparable scientific depth and delivery.

Benefits

• Competitive compensation: 34,000–47,000 PLN/month, stock options, and an annual bonus.

• Flexible benefits allowance.

• Office in the city center and a friendly, non-corporate atmosphere.

• Real ownership and visible impact.