L

Senior Data Scientist — Product

Legora is redefining how legal work gets done. Not built for lawyers, built with them. We work alongside the world’s best legal teams, who expect excellence, precision, and speed, and we hold ourselves to the same bar.

legora · На сервисе с: 03.09.26 02:06

Зарплата не указанаВеликобританияLondonОфис

About Us

Legora is redefining how legal work gets done. Not built for lawyers, built with them. We work alongside the world’s best legal teams, who expect excellence, precision, and speed, and we hold ourselves to the same bar.

Our AI-native workspace lets legal professionals move faster, think more clearly, and operate with sharper precision. By analysing thousands of documents in minutes and powering end-to-end workflows, we cut through complexity, teams can focus on what matters: judgment, strategy, and outcomes.

1,000+ customers across 50+ countries trust us, including Cleary Gottlieb, Goodwin, Linklaters, White & Case, Dentons, and Barclays. We’ve scaled to $100M+ in ARR, with teams across Europe, North America and APAC, and continue to expand through acquisitions including Qura, Walter AI and Graceview.

We partner with world-class performers: including Aaron Judge and the New York Yankees, Ludvig Åberg (and his caddie), and campaigns featuring Jude Law.

Joining Legora means three things.

  • We lean in: ownership over titles, outcomes over intentions.

  • We fight for excellence: high standards, direct, ego-free feedback.

  • We grow together: as a team and with our customers.

Mission before ego. Everyone contributes. No one coasts.

If you’re driven by impact, pace, and raising the bar. This is the place.

The role
As a Senior Data Scientist for Product at Legora you will turn data into decisions. You'll sit close to the business, taking questions end-to-end: shaping the metric, modelling the data in dbt, running the analysis, and making the recommendation. You'll pull in new data sources when you need to. Insights are useful; impact is what we hire for.

We're an AI-first data team. We believe the data function should be redesigned around what AI now makes possible, not retrofitted with it, and we want someone excited to help define what that looks like in practice.

There's no single profile we hire for. Some of us are strongest at data modelling and analytics engineering, some at experimentation and causal inference, some at machine learning, some at stakeholder influence. You'll likely be excellent at one or two of these and competent across the rest. That's the bar.

We're a small, centralised team supporting the whole company, hiring for the person, not the seat. Depending on your strengths and where we have the biggest gap when you join, you could be embedded primarily with:

  • Product: instrumentation, feature adoption, user behaviour, A/B testing, shaping the roadmap with PMs and designers.

  • Finance & RevOps: ARR, NRR, forecasting, board reporting, pricing analytics across a 40-country footprint, and unit economics for an AI-native product.

  • Growth & Marketing: acquisition funnels, attribution, campaign measurement, lifecycle analytics, and what actually moves enterprise legal buyers.

  • GTM & Customer Success: pipeline analytics, customer health, expansion signals, and retention drivers in a category that didn't exist three years ago.

You'll partner directly with leaders across Product, Engineering, Finance, and GTM, most of whom are unusually data-fluent and will happily open a SQL editor with you. Your work will directly influence how we prioritise, how we sell, how we price, and how we build.

What you will be doing

  • Partner with stakeholders across Product, Finance, GTM, Growth, and beyond to translate ambiguous questions into structured analyses and clear recommendations.

  • Define the metrics that matter, design the experiments or analyses that test them, and measure the impact of what we ship.

  • Conduct deep-dive analyses on the questions that move the business, and proactively surface the questions nobody is asking yet.

  • Model the data you need for your work in dbt, pulling in new sources when necessary, and partner closely with data engineering on anything that needs to scale beyond your immediate use case.

  • Build dashboards and reporting that scale beyond you, so the company can answer its own questions where possible.

  • Help shape how the data team operates as we scale: standards, tooling, ways of working.


What you'll need

  • Strong proficiency in SQL and Python.

  • Solid grasp of data modelling and what it takes to build analytical work that is reliable and trusted.

  • Excellent communication skills and the confidence to influence decisions through data storytelling, including pushing back when the data doesn't support what someone wants to do.

  • Genuine depth in at least one of the following, with competence across the rest and curiosity to grow:

    • Data modelling and analytics engineering (dbt, dimensional modelling, semantic layers, self-service)

    • Experimentation and causal inference (A/B test design, quasi-experiments, statistical rigour)

    • Machine learning and applied data science (forecasting, prediction, segmentation, evaluation)

    • Product analytics and metric design (funnels, cohorts, adoption frameworks, North Star metrics)


Nice to have

  • Experience in a product-led or SaaS environment, ideally B2B.

  • Hands-on experience with our stack: Snowflake, dbt, Hex, Dagster.

  • Prior experience as an early data hire at a fast-growing company, i.e. you've built the muscle, not inherited it.

Most importantly, you are someone who

  • "Gets stuff done" and understands building a $10bn company isn't always glamorous and takes hard work and long hours.

  • Thrives in a fast-paced environment where the answers aren't always clear and processes are few.

  • Is genuinely domain-curious. You don't need to have worked on every part of a business, but you should be excited to.

 

Legora is an Equal Opportunity Employer

At Legora, we believe great teams are built on diversity of thought and experience. We’re proud to be an equal opportunity employer and committed to creating an inclusive, high-performance culture where everyone can do their best work. We welcome people of all backgrounds and don’t discriminate based on race, color, religion, national origin, gender, gender identity or expression, sexual orientation, age, disability, veteran status, or any other characteristic protected by law.

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S

Staff Data Scientist, Watchlist

socureНа сервисе с: 24.09.26 18:12
Зарплата не указанаСШАУдалёнка

Why Socure?

Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.

We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.

WHY SOCURE?

Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.

We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won't be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.

ABOUT THE ROLE

We are looking for a Staff Data Scientist to join Socure's Watchlist Data Science team. Watchlist sits at the heart of global AML compliance — our platform screens hundreds of millions of entities in real time across sanctions lists, PEP databases, and adverse media sources for banks, fintechs, and payment companies worldwide.

As a Staff Data Scientist, you will work on the hardest problems in entity matching and classification: scaling our patented real-time matching engine, building advanced Natural Language Processing (NLP) models for Named Entity Recognition (NER) and Information Extraction, and bringing next-generation research to production. This is a senior individual contributor role with broad technical ownership and direct impact on a product that helps the world's financial institutions manage sanctions and AML risk.

WHAT YOU'LL DO

Data Quality & Enrichment

  • Improve the quality, coverage, and freshness of Watchlist's underlying data through next-generation ingestion pipelines.

  • Design and execute rigorous data quality analysis pipelines to identify anomalies, evaluate dataset health, and ensure high-fidelity inputs for downstream model training.

  • Apply NLP and AI to classify and enrich raw source data into normalized schemas — extracting structured entity attributes from unstructured sanctions, PEP, adverse media, and enforcement sources.

  • Expand multilingual capabilities to support global screening across Latin and non-Latin scripts.

Entity Resolution

  • Build and improve NLP systems that consolidate how watchlist identities are represented. Developing Information Extraction and Named Entity Recognition (NER) pipeline to deduplicate entities across lists and resolve aliases into canonical profiles..

  • Develop approaches to handle how entity profiles change over time as names, aliases, and sanctions status evolve.

  • Measure and benchmark entity resolution quality, driving continuous improvement in coverage and accuracy.

Match Engine & Risk Scoring

  • Design and scale advanced NLP models and algorithms that perform real-time name matching and identity classification across diverse, multilingual unstructured data sources.

  • Build multi-signal risk scoring that combines name similarity, entity type, geography, list type, and other attributes into unified, calibrated risk scores.

  • Maintain and improve benchmarking frameworks, golden datasets, and regression tests that keep the match engine at the highest levels of recall and precision.

Analytics, Tuning & Evaluation

  • Build models and analytics that help customers tune their screening thresholds to the right operating point for their risk appetite and entity mix.

  • Develop backtesting and counterfactual analysis capabilities so customers and internal teams can understand how model or threshold changes would affect screening outcomes.

  • Design evaluation frameworks for AI-powered autonomous decision systems — defining correct behavior, calibrating confidence thresholds, and monitoring for drift in production.

AML Risk Detection

  • As Watchlist expands into payment screening, build the mathematical analysis and feature engineering needed to detect AML risk patterns across transaction data and payment message fields.

  • Develop and maintain the AML taxonomy and risk signal library that underlies Watchlist's classification and detection capabilities.

  • Apply graph-based methods to surface indirect risk exposure — identifying entities connected to sanctions risk even when they are not directly listed.




Research & Technical Leadership

  • Lead technical initiatives across Watchlist Data Science and shape the team's long-term approach to entity matching, enrichment, and AI.

  • Collaborate closely with Product and Engineering to translate research into production-grade systems at scale.

  • Stay current with advances in NLP, large language models, and entity resolution; prototype and deploy relevant techniques (e.g., advanced NER, LLM-based extraction) to AML use cases.

  • Mentor peers and contribute to a culture of technical rigor and continuous improvement.

WHAT YOU BRING

  • Master's or PhD in Computer Science, Computational Linguistics, Statistics, Applied Mathematics, or a related field; or equivalent professional experience.

  • 7+ years of experience in data science or machine learning, with meaningful work in NLP, entity resolution, or information extraction.

  • Experience in AML, sanctions screening, adverse media, or financial crime detection is strongly preferred.

  • Hands-on experience building and deploying NLP pipelines for entity extraction, named entity recognition, and record linkage at production scale.

  • Familiarity with multilingual NLP and non-Latin script processing is a strong plus.

  • Experience with LLMs and agentic AI frameworks (e.g., LangChain/LangGraph) is a plus.

  • Strong proficiency in Python and major ML libraries (PyTorch, spaCy, HuggingFace Transformers).

  • Strong SQL proficiency and experience with large-scale data pipelines and production ML systems.

  • Excellent communication skills — able to translate model performance tradeoffs into compliance and business language for non-technical audiences.

Note: We cannot provide Sponsorship at this time.

You must be located in one of our talent hubs: New York, San Francisco, Seattle, or Miami.

Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need an accommodation during any stage of the application or hiring process — including interview or onboarding support — please reach out to your Socure recruiting partner directly.

Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.



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сбер. it

Senior/middle data scientist (Центр Модельных Рисков)

сбер. itНа сервисе с: 24.09.26 17:03
Зарплата не указанаРоссияМоскваОфис

Управление Модельных Рисков - «by-design» уникальное подразделение, которое «видит» все AI/ML модели Банка, понимает процессы/точки принятия решений моделями, кривая обучения у наших сотрудников «круче», чем в подразделениях, разрабатывающих модели.

Наша цель – помочь Банку зарабатывать больше и улучшать клиентский опыт за счет повышения эффективности моделей и их использования в Бизнес процессах.

Мы ищем DS/Quant с экспертизой на стыке финансовой математики, торговых алгоритмов и ML для работы в напралении Модельного Риска Торговой Книги. Вы будете выступать ключевым звеном контроля качества моделей и агентов, используемых в оценке рисков и ценообразовании финансовых инструментов.

Обязанности

  • оценка качества моделей ClassicML, работающих на финансовых рынках, в том числе: торговые алгоритмы, оценка эластичности по цене, кластеризация клиентов и т.д.
  • проведение независимой оценки качества моделей прайсинга финансовых инструментов, а также риск-моделей (включая VaR, CVA, PFE)
  • анализ корректности математических предпосылок, устойчивости моделей и их соответствия бизнес-требованиям и рыночным условиям
  • оценка и контроль рисков агентов, работающих в торговых алгоритмах, процессах прайсинга финансовых инструментов и оценки рыночных рисков
  • создание инструментов для автоматизации процессов мониторинга моделей и агентов с использованием Python
  • подготовка аналитических заключений по результатам валидации, формирование рекомендаций по доработке моделей
  • презентация результатов работы, аргументация выводов и защита предложенных решений перед разработчиками моделей, риск-менеджментом и бизнес-заказчиками.

Будет плюсом:

  • навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом.

Требования

  • знание теории вероятностей, математической статистики и случайных процессов
  • опыт работы на стороне разработки или валидации ClassicML моделей
  • уверенные навыки программирования на Python, опыт разработки структурированного и поддерживаемого кода
  • знание базовых принципов построения моделей прайсинга производных финансовых инструментов
  • опыт работы с количественными моделями в финансах или смежных областях будет преимуществом
  • участие в летних школах или курсах: Vega, ЦМФ – также является преимуществом.

Условия

  • работа в удобном и современном офисе недалеко от станции метро Кутузовская
  • ежегодный пересмотр зарплаты, годовая премия
  • корпоративный спортзал и зоны отдыха
  • более 400 образовательных программ СберУниверситета для профессионального и карьерного развития
  • расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа
  • гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ
  • подписка Прайм с возможностью совместного использования на трёх близких
  • вознаграждение за рекомендацию друзей в команду Сбера.
Соц.сети
Н

Senior ML Engineer / MLOps Engineer

Неизвестный работодательНа сервисе с: 24.09.26 16:27
Зарплата не указанаПольшаУдалёнка

#вакансия #vacancy
🚀 Senior ML Engineer / MLOps Engineer — 100% Remote
Must-have:
• Strong Python
AWS / SageMaker
MLOps, CI/CD
Docker + Kubernetes
• ML lifecycle tools: MLflow / Kubeflow / SageMaker Pipelines
• Production experience with ML & LLM applications
Generative AI / LLMOps
• PySpark / Apache Spark
• FastAPI / Flask
• Model training, fine-tuning, evaluation & optimization
📍 100% Remote | 🇬🇧 English B2+
📅 Duration: 3 months + extension
💰 Rate: TBD
⏰ Full-time
📩 Для подачи присылайте CV в telegram: ••••••••

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