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Consultant designs and implements cloud data platforms and pipelines for clients, translating business needs into scalable architectures using AWS/Azure/GCP, SQL, Python, and big-data tools.
Build and deploy ML models for risk and intelligence products at scale, focusing on fraud detection and network analysis using Python, TensorFlow/PyTorch, and BigQuery.
Designs and deploys production-grade AI agents using LLMs, agent orchestration frameworks, and enterprise systems to automate complex workflows via natural language interactions.
Facilitates Agile ceremonies (e.g., sprint planning, retrospectives) and removes blockers for a government team delivering climate-tech solutions, ensuring backlog accuracy and team alignment with Scrum/Kanban/SAFe.
Ищем эксперта в нашу команду для участия внедрения рисковых моделей Банка на Индийском направлении. Внедряемые модели участвуют в процессах кредитования и имеют высокое влияние, поэтому с одной стороны - скорость…
Manage reliability and performance of a Hadoop cluster for operational data storage import substitution using Hadoop, Postgres, Spark, Airflow, and Trino.
Designs and develops firmware for embedded systems in emergency lighting and IoT products, focusing on microcontrollers, wireless communication, and DALI/HIVE technologies to create reliable, innovative solutions deployed globally.
Data Security Solutions Architect at a cybersecurity VAD, engaging clients with presentations/demos, designing data security solutions, and supporting pre/post-sales. Core tech includes HSM, DLP, Imperva, PKI, data masking, and enterprise data architecture.
Чем предстоит заниматься: Выполнение задач полного жизненного цикла (от сбора требований до выкатки на установку в прод) Разработка/доработка витрин/решений и их автоматизация через оркестраторы Изменение алгоритма…
ML Engineer builds, tests, and deploys risk models for Sberbank’s India lending unit using Python, Spark, and MLOps pipelines in Kubernetes.
Builds and maintains big data pipelines, processes large-scale data using Hadoop/Spark, and develops/optimizes ETL workflows with Python/SQL. Supports existing systems and implements new requirements in a remote Agile environment.
A Salesforce QA Engineer tests and validates Salesforce implementations, ensuring software quality via test automation, defect tracking, and collaboration with clients/vendors. Core tools include Salesforce, HP ALM/UFT, and Jira in an Agile sprint environment.
Builds and optimizes data pipelines using PySpark, SQL, and the Hadoop ecosystem to process large datasets and support business analytics.
Leads a team managing data engineering operations, production support, and cloud-based data pipelines/warehouses (Snowflake, AWS Glue) while migrating legacy Oracle/MS-SQL/Hive workloads and improving data quality/automation.
Lead algorithm development and analyze complex datasets for clients in sectors like national intelligence using machine learning, NLP, and statistical analysis.
A mid-level Data Scientist who provides statistical and mathematical support to analyze data, design analysis projects, and generate insights for leadership to inform strategic decisions, using tools like SQL, Pandas, and Jupyter Notebook.
Coordinates IT projects for a federal election agency, tracking risks, milestones, and deliverables using Agile tools like Azure DevOps and MS Suite.
Senior Data Scientist designing and implementing ML solutions for anomaly detection, root cause analysis, and predictive models on terabytes of streaming datacenter network data using Python, Golang, and big data tools like Spark and Flink.
The Big Data Engineer will design and maintain batch and real-time data warehouse solutions to support business decision-making. The role involves building ETL pipelines, developing data products, and applying data modeling and machine learning techniques to large datasets.
The Big Data Engineer will design and maintain batch and real-time data warehouse systems, build data products, and develop ETL pipelines to support business decision-making. The role involves collaborating with stakeholders to transform large datasets into actionable insights using distributed computing engines and data modeling techniques.
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