Data Specialist/Scientist - PageGroup SSC

• New challenge in a International company. SSC. English. Barcelona.
• Python, SQL, Machine Learning

Are you looking for a place to work that inspire and challenge you? A place to unleash your potential? Then the PageGroup Barcelona Shared Service Center (SSC), with its flexible, open culture and meritocratic structure is the place for you.

https://www.pagepersonnel.es/clientprofile/pagegroup-shared-services-centre

• Data Profiling & Monitoring:




• Develop and enhance data profiling engines to assess completeness, validity, and integrity of datasets.


• Collaborate on AI-driven cataloguing projects, including a Purview Proof of Concept (POC).


• Monitor data lake quality and performance using tools such as Databricks/Cloud based data platforms.




• Machine Learning for Data Quality:




• Apply ML techniques (e.g., Random Forest) to improve duplicate identification and matching.


• Build models to validate taxonomy mapping using LLMs, similar to inferring roles/departments based on job titles.




• Automation and API Integration:




• Automate data update processes using APIs, reducing reliance on manual scripts, extracts, and CSVs.


• Design scalable solutions for automated data reconciliation and integrity checks.




• Data Quality Analysis:




• Conduct detailed data quality assessments, measuring completeness, validity, and consistency across datasets.


• Identify gaps in data pipelines and propose actionable solutions.

• Meal vouchers


• Bonus


• Remote working (2 days per weeks)


• Medical insurance (after 6 months)


• Life insurance


• Private pension (after 2 years)


• Flexible compensation (after 6 months)


• July & August 36h per week


• Holidays per year - 25 days


• 20 working days per year to work from abroad


• EAP - since day one

Expertise

• ML Model Development and Evaluation:




• Understanding statistical distributions and probabilities is key to choosing the right features, algorithms, and evaluation metrics for ML tasks (e.g., precision, recall, F1-score).


• Advanced tasks like enhancing duplicate detection or inferring roles with LLMs may involve probabilistic approaches.




• Data Quality Analysis:

• Quantifying and diagnosing data completeness, validity, and integrity often require statistical tests and descriptive analytics.

• General Problem-Solving:

• Statistical reasoning aids in diagnosing anomalies, reconciling datasets, and creating predictive models.

Must have

• Proficiency in Python (essential for ML and automation tasks).


• Strong understanding of statistics and probability, including hypothesis testing, regression, and probabilistic reasoning.


• Experience with machine learning techniques (e.g., Random Forest, clustering, or NLP-based models).


• Solid grasp of data quality concepts: completeness, validity, reconciliation, and profiling.


• Strong problem-solving skills and the ability to design scalable solutions.

Should-Have:

• Hands-on experience with Databricks (for data lake monitoring and ML implementation).


• Familiarity with data cataloguing tools like Purview or similar platforms.


• Working knowledge of SQL and large datasets.

Could-Have:

• Experience with R for statistical analysis or visualization.


• Knowledge of LLMs for advanced text or taxonomy-related projects.


• Familiarity with data governance frameworks or compliance requirements.