Shell biggest freshers hiring for Data Science Analyst role 0–2 years of experience candidates apply Full Time

As a Data Science Analyst within the Digital Process Twin (DPT) portfolio, you will apply data science methods to process event data to support analytical modeling, process insights, and data-driven decision-making. Working alongside experienced data scientists, engineers, and process analysts, you will help prepare, analyze, and visualize data to reveal opportunities for improving business process efficiency and performance.

If you are passionate about exploring process event data, developing analytical insights, and learning how data science enhances operational agility, we invite you to join us as a Data Science Analyst contributing to Shell’s digital process transformation journey.

Role and Responsibilities

• 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.

Qualification

• 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.