Medior Databricks AI/ML Specialista
Salary not listed • Budapest • Full-time
Posted 24d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
PwC is seeking a Medior Databricks Specialist to join their Data & AI Platform team. This role involves working on innovative projects that drive significant technological change for partner companies. You will contribute to building enterprise data platforms, developing predictive and machine learning models across various sectors, and integrating generative AI solutions into existing data infrastructures. This is an opportunity to learn from leading experts in Hungary, shape data and analytical operating models, and gain exposure to diverse industry challenges.
Responsibilities
- Develop data transformation and preparation pipelines in Databricks (PySpark, SQL) to create datasets for analysis and modeling.
- Create, transform, and validate analytical variables (features) for machine learning and statistical models.
- Develop, train, and evaluate machine learning models on Databricks, supporting the end-to-end ML lifecycle, potentially using MLflow.
- Independently and collaboratively execute data analysis and data science projects, guiding junior team members and answering business questions with data.
- Integrate generative AI and advanced analytical solutions, such as vector databases and RAG components, into Databricks data platforms.
- Collaborate closely with business units and internal stakeholders to identify and implement new data-driven and AI use cases.
- Take ownership of Proof of Concept (PoC) and pilot projects from planning to delivery, in close cooperation with senior experts.
Requirements
- Minimum 2-3 years of relevant data engineering, data science, or analytics experience, with at least 1 year specifically on the Databricks platform.
- Proficient Python and SQL skills, with practical experience in PySpark/Spark frameworks.
- Proficiency in cleaning, transforming, and preparing large datasets for analytical purposes (feature engineering, aggregations, data quality).
- Hands-on experience developing, training, and evaluating ML models (e.g., scikit-learn, MLflow), with knowledge of the model development lifecycle.
- Fundamental knowledge and usage of Databricks ecosystem components like Delta Lake, Databricks Notebooks, Databricks Workflows, and the Lakehouse/Medallion architecture.
- Practical knowledge of at least one major cloud platform (Azure, AWS, or GCP).
- Strong analytical thinking and a commitment to writing clean code, with knowledge of version control (Git).
- Excellent teamwork and independent responsibility skills, with a proactive, solution-oriented approach.
- Confident, at least intermediate, English language proficiency.
Benefits
- Competitive compensation
- Opportunity for rapid development
- Challenging tasks and a dynamic work environment
- Diverse tasks and work with large enterprise clients on domestic and international projects
- Deepen knowledge in Databricks analytics, data engineering, data science, and machine learning capabilities, up to generative AI use cases.