CX Data Scientist
- Budapest
- Állandó
- Teljes munkaidő
- Design, develop, and maintain scalable data pipelines and ETL/ELT processes on Google Cloud Platform (GCP) to ingest, process, and store data from diverse sources, ensuring data quality and reliability.
- Leverage expert-level SQL and Python skills for complex data extraction, transformation, cleansing, and analysis, with a strong focus on preparing data for analytical and machine learning applications.
- Develop, deploy, and monitor machine learning models and AI-driven solutions (e.g., predictive analytics, natural language processing for chatbots, anomaly detection) using GCP AI Platform (Vertex AI) and other relevant AI APIs.
- Architect and manage data structures and tables within GCP (e.g., BigQuery, Cloud SQL, Spanner), optimizing for performance, cost, and accessibility for various CX use cases.
- Create and automate the generation of insightful dashboards and reports (e.g., using Looker, Power BI, or Qlik Sense) for CX performance tracking, connected vehicle data exploration, and ad-hoc analyses.
- Collaborate closely with CX Performance Managers, Analysts, Product Owners, and other stakeholders to understand data requirements, define project scope, and deliver data solutions that meet business needs.
- Explore and analyze large datasets, including eg. vehicle maintenance data, to uncover trends, patterns, and insights that can inform CX strategy and product development.
- Lead and support data projects involving the use of AI APIs (e.g., Google Cloud AI APIs like Dialogflow, Natural Language API, Vision API) to build innovative CX tools and functionalities.
- Ensure data governance, security, and compliance best practices are implemented and adhered to for all data solutions, particularly concerning customer data and PII.
- Provide technical guidance and mentorship to other team members on data engineering best practices, GCP services, and AI/ML techniques.
- Stay current with emerging technologies and advancements in data engineering, data science, AI/ML, and the Google Cloud Platform ecosystem, and advocate for their adoption where beneficial.
- Document data architectures, data flows, model specifications, and processes to ensure clarity, maintainability, and knowledge sharing within the team.
- Degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a comparable quantitative field (Bachelor's degree required; Master's or PhD is desirable).
- Proven hands-on experience as a Data Engineer or Data Scientist, with a significant portfolio of projects involving data pipeline development, data modeling, and deploying solutions on cloud platforms.
- Expert proficiency in SQL and Python, including common data science and machine learning libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
- In-depth knowledge and practical experience with Google Cloud Platform (GCP) data services, including but not limited to BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Cloud Functions, Vertex AI, and Looker.
- Strong understanding of data warehousing principles, database design, and data modeling techniques (both relational and non-relational).
- Experience in developing and deploying machine learning models into production environments; familiarity with MLOps principles and tools is a strong advantage.
- Proficiency in building and automating dashboards and reports using tools like Looker, Power BI, Tableau, or Qlik Sense.
- Experience working with APIs for data ingestion and for leveraging AI services.
- Familiarity with connected vehicle data, dealer invoice data, automotive industry data, or customer experience metrics (e.g., NPS, CSAT) is a significant plus.
- Excellent analytical and problem-solving skills, with the ability to translate complex business problems into technical solutions and a keen attention to detail.
- Strong communication and interpersonal skills, with the ability to effectively collaborate with both technical and non-technical stakeholders.
- Self-motivated, proactive, and capable of working independently with minimal supervision, as well as effectively within a team in an agile environment.
- Knowledge of data governance and data security best practices, especially within a cloud context.