Shell Data Science Job Openings 2025 – Apply Soon!

Qualifications

What you bring

• 0–2 years of experience in data analytics or data science, including exposure to data preparation, analysis, or visualization.
• Basic understanding of machine learning or statistical techniques applied to structured or event data.
• Experience with Python, SQL, and data manipulation libraries (e.g., pandas, NumPy).
• Exposure to data visualization tools and frameworks such as Power BI, Tableau, Plotly Dash, or Matplotlib/Seaborn.
• Familiarity with process event or behavioral data and understanding of event logs (case IDs, activities, timestamps) is desirable.
• Exposure to process-mining platforms (e.g., Celonis EMS, SAP Signavio, or PM4Py) is an advantage.
• Knowledge of data quality, ETL concepts, or pipeline design is preferred.
• Enthusiasm to learn AI/ML, process analytics, and MLOps practices in enterprise environments.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related quantitative field.
• Proficiency in Python and SQL for data analysis and transformation.
• Basic knowledge of machine learning or predictive modeling concepts.
• Strong analytical and problem-solving mindset with attention to detail.
• Ability to learn and apply process-oriented data science methods.
• Exposure to process mining and understanding of event-based data.
• Experience using data visualization tools and frameworks such as Power BI, Tableau, or Plotly Dash for process-performance reporting
• Understanding of data quality, data wrangling, and feature engineering.
• Exposure to Celonis EMS, SAP Signavio, or open-source process-mining frameworks (PM4Py, ProM).
• Academic or project experience in process simulations or operations research.
• Awareness of professional data science communities and willingness to engage for learning and growth.

What’s the role

• Support the preparation, cleansing, and transformation of process event data for analysis and modeling.
• Assist in exploratory data analysis (EDA) to identify trends, patterns, and anomalies in process data.
• Contribute to data visualization, dashboards, and reports to communicate insights using tools such as Power BI, Tableau, or Plotly Dash.
• Collaborate with data scientists to design and test features and metrics for process performance and prediction models, gaining exposure to analytical design principles and learning from implementation feedback.
• Participate in the creation and maintenance of process data models and pipelines, ensuring data quality and consistency.
• Work with cross-functional teams to integrate data from multiple enterprise systems (e.g., SAP, Salesforce, ServiceNow) for process analysis.
• Support process mining initiatives by preparing event logs and assisting with analyses on platforms like Celonis EMS or SAP Signavio.
• Contribute to documentation, testing, and quality assurance for analytical deliverables.
• Develop foundational understanding of Responsible AI and Explainable AI (XAI) concepts.