Python állások I. kerületben (adatrögzítő)

• Client-facing communication
• Participation in pre-sales discussions
• Collaboration with sales and customer success teams
• Communication with clients' technical teams
• Solution design and delivery
• Design and configuration of data labeling and processing pipelines using Python-based tools

• Főbb feladatok
• Termékfejlesztési kérdések lefordítása mérhető és elemezhető kutatási kérdésekké.
• A kérdés megválaszolásához szükséges adatok és adatforrások meghatározása.
• Külső, megvásárolt fogyasztói, piaci, retail, termékbevezetési, trend- és egyéb adatbázisok elemzése.

• Configuration of the P&P platform end to end across frontend and backend
• Tuning and optimization of forecast and attribution models to match client data and business logic
• Writing custom business logic in SQL, Python, or Go for non-standard configuration

• Establish data feeds with custodian banks, data vendors, and other service providers
• Support senior leadership, clients, and the broader Data Team in executing analyses and providing data-driven answers to questions
• Fully automate and centralize existing data integration processes

• Search, collect, consolidate, and validate financial and non-financial data across departments and systems
• Design, build, maintain, and automate dashboards and underlying data pipelines to provide timely, accurate insights to stakeholders

• Co-design and implement QA workflows using statistical diagnostics, validation rules, and anomaly detection techniques
• Apply AI/machine learning tools to identify inconsistencies, missing values, or suspicious patterns in large-scale asset datasets

• Ownership of data engineering features, architecture, and code quality
• Design, implementation, and maintenance of Databricks-based data pipelines and workflows
• Building and optimization of ETL/ELT processes using Apache Spark on Databricks

Performing tasks related to the position....

• Development and refinement of core sports betting models for predicting match outcomes
• Building and maintenance of player statistics models analyzing player performance
• Design and implementation of risk management models to optimize exposure

• Dashboard development with Power BI to visualize sustainability data and metrics
• Data integration from various sources including internal databases, external APIs, and third-party platforms
• Client reporting with customized visual analytics solutions

• Identifying, developing, and implementing AI-driven solutions
• Supporting colleagues in creating AI-powered solutions including agents, workflow automations, and analytics tools
• Monitoring developments in AI, Generative AI, and digital productivity tools

• Analysis of global service quality data including non-conformance costs, corrective and preventive actions, customer satisfaction, NPS, and audit findings
• Transformation of analysis into clear conclusions and prioritized recommendations for regions, central functions, and management

• Measuring performance of multi-channel patient recruitment campaigns
• Optimizing multi-channel patient recruitment campaigns
• Forecasting performance of multi-channel patient recruitment campaigns
• Translating recruitment data into actionable insights
• Accelerating enrollment
• Reducing site burden

• Improvement of repetitive processes using AI
• Organization and checking of data used by the Operations team
• Connecting everyday tools to reduce manual work
• Experimentation with AI tools, chatbots and workflows
• Troubleshooting and solving smaller operational tasks

• Experience in different data science topics including machine learning, LLM, deep learning
• Independent delivery of data science projects with e2e product-oriented mindset
• Direct engagement with clients to understand business objectives and translate into technical requirements

• Design and implement modern data architectures and lakehouse solutions
• Provide technical leadership throughout the project lifecycle
• Collaborate with business stakeholders
• Create effort estimations
• Provide technical expertise during the pre-sales phase
• Mentor 3-6 person project teams

• Designing, implementing, testing, and optimizing reliable, efficient, and high-quality data pipelines
• Automating the extraction, loading and transformation of data from a wide variety of sources
• Developing solutions for the automated acquisition of data from source systems

• Development, testing, and maintenance of data pipelines to extract, transform, and load (ETL) data from various sources
• Writing high-quality, maintainable Python code
• Participation in code reviews
• Participation in technical discussions for tool selection and architectural decisions

• Design, develop, and implement complex, scalable Lakehouse solutions on the Databricks platform across major cloud environments (Azure, AWS, or GCP)
• Design, build, and optimize modular data pipelines using Delta Lake to ensure data efficiency, reliability, and quality