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Senior Data Scientist

At FourKites we have the opportunity to tackle complex challenges with real-world impacts. Whether it's medical supplies from Cardinal Health or groceries for Walmart, the FourKites platform helps customers operate global supply chains tha…

fourkites · На сервисе с: 25.08.26 10:13

Зарплата не указанаИндияУдалёнка

At FourKites we have the opportunity to tackle complex challenges with real-world impacts. Whether it's medical supplies from Cardinal Health or groceries for Walmart, the FourKites platform helps customers operate global supply chains that are efficient, agile and sustainable.

Join a team of curious problem solvers that celebrates differences, leads with empathy and values inclusivity.

As a Senior Data Scientist, you will build and own machine learning models that power core prediction problems across the FourKites platform — including ETA/ATA forecasting and message-based status extraction. You will work end-to-end, from data pipeline to production deployment and monitoring, turning noisy real-world logistics data into models that run at scale and directly move the needle on customer outcomes. You will work closely with product, engineering, and operations teams, hands-on building and shipping models yourself while also guiding the technical direction of other data scientists on the team.

What you'll be doing:

  • Design, build, and productionize ML models for problems like ETA/ATA prediction, using regression, classification, and time-series forecasting techniques
  • Develop NLP/LLM-based extraction pipelines for message-based ETA and status updates (text extraction, entity recognition)
  • Own models end-to-end: data pipeline → training → deployment → monitoring → retraining
  • Work with noisy, real-world logistics and supply chain data (GPS pings, check calls, carrier data) rather than clean, pre-processed datasets
  • Diagnose gaps between offline evaluation performance and live production accuracy, and drive fixes
  • Build and maintain automated training/retraining pipelines using orchestration tools such as Airflow
  • Set up and maintain model monitoring and observability (e.g., Grafana) to catch drift and degradation proactively
  • Replace manual or rule-based processes with ML-driven automation (e.g., automating manual check calls)
  • Translate model performance improvements into business impact — operational savings, efficiency gains, and deal-relevant outcomes
  • Mentor and guide other data scientists/engineers on technical approach and best practices
  • Make build-vs-buy and architecture tradeoff decisions independently

About the team:

Our product and engineering teams are dedicated to providing the industry's best-in-class end-to-end supply chain visibility platform. We are committed to building a high-performing, ML-driven team that turns supply chain data into automated, proactive action — and we want you to help lead that effort.

Who you are:

  • Strong ML fundamentals across regression, classification, and time-series forecasting
  • NLP experience — text extraction, entity recognition, or LLM-based extraction
  • Production ML experience — you've shipped models serving real traffic, not just built POCs or notebooks
  • Strong Python and SQL skills — pandas, scikit-learn, and comfort querying large datasets (Redshift/Snowflake a plus)
  • Experience with cloud and data infrastructure — AWS (S3, EC2), and orchestration tools like Airflow for training/retraining pipelines
  • Experience setting up or working with model monitoring and observability tooling (Grafana or similar)
  • Comfortable working with noisy, real-world data rather than clean, curated datasets
  • Experience diagnosing and closing the gap between offline evaluation results and live production performance
  • A track record of replacing manual/rule-based processes with ML solutions
  • Ability to translate model output into business value and communicate that impact to non-technical stakeholders
  • Experience collaborating cross-functionally with product, engineering, and operations teams
  • Experience mentoring or guiding other data scientists or engineers
  • Ability to make build-vs-buy and architecture tradeoffs independently
  • A track record of reducing manual intervention or turnaround time through automation
  • Excellent oral and written communication skills

Nice to have:

  • Experience in logistics, supply chain, or transportation
  • Familiarity with real-time/streaming data (Kafka)
  • Exposure to LLM/GenAI applications in production

Who we are:

FourKites, the leader in AI-driven supply chain transformation for global enterprises and pioneer of real-time visibility, turns supply chain data into automated action. FourKites Intelligent Control Tower® breaks down enterprise silos by creating a real-time digital twin of orders, shipments, inventory and assets. This comprehensive view, combined with AI-powered digital workers, enables companies to prevent disruptions, automate routine tasks and optimize performance across their supply chain. FourKites processes over 3.2 million supply chain events daily — from purchase orders to final delivery — helping 1,600-plus global brands prevent disruptions, make faster decisions and move from reactive tracking to proactive supply chain orchestration. For more information, visit www.fourkites.com.

FourKites provides competitive compensation with stock options, outstanding benefits and a collaborative culture for all employees around the globe. To help you be your best, we have 5 global recharge days, in addition to generous PTO and standard holidays. Parental leave for all parents, an annual wellness stipend and volunteer days also provide you with time and resources for self care and to care for others. Throughout the year, FourKites sets aside time during the workday to learn and celebrate diversity. We're always listening for new ways to support everyone in and out of the office.

Benefits:

  • Medical benefits start on first day of employment
  • 36 PTO days (Sick, Casual and Earned), 5 recharge days, 2 volunteer days
  • Home Office set ups and Technology reimbursement
  • Lifestyle & Family benefits
  • Mental Wellness support and guidance
  • Ongoing learning & development opportunities (Professional development program, Toast Master club, etc.)

FourKites is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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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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