Rystad Energy Interview Experience & Selection Process (On-Campus)

In this article, I’ll walk you through my complete interview journey with Rystad Energy, from applying, to getting selected as a Data Analyst Intern, to finally receiving a full-time offer.

If you’re preparing for a Rystad Energy interview, I’ll also share insights and preparation tips along the way.

Short intro about me

I graduated in Data Science & Engineering from MIT Manipal (Class of 2025). Before joining Rystad, I worked as a Business Analytics Intern at Landmark Group, where I gained hands-on experience in data analysis, geospatial insights, and AI-driven workflows.

That background helped me build the right foundation before stepping into the Rystad process.

Applying for the job

In August 2024, Rystad Energy visited our campus for their placement drive. They were hiring for three roles:

• Data AnalystTechnical role: coding in Python, Power BI, etc.
• Research AnalystWriting commentaries and reports, working with clients, finding data.
• Data & Research AnalystA bit of both. The process is the same as for Data Analyst.

We had to submit our applications through the campus job portal.

I applied for the Data Analyst role, so this article focuses mainly on that track. However, the overall selection process was largely the same across all three roles.

The selection process

The entire recruitment process had three major stages:

1. Online test / assessment
2. Video round
3. Interviews (technical, then techno-managerial / HR)

Online test / assessment

For the Data Analyst and Data & Research Analyst roles

The test was divided into four main sections: Python, Pandas, SQL, and Machine Learning with aptitude and other miscellaneous topics. Some sample MCQ-style questions you can expect for the Data Analyst role:

For the Research Analyst role

The assessment was lighter and focused mainly on basic mathematics, basic aptitude, and basic English.

Video round

The next stage was the video round. It was pretty straightforward: you were given 4 pre-defined questions and had to record your answers on video.

The platform gave ample time to think, prepare, and re-record before submitting, so the key was to stay calm, structured, and confident in your delivery.

1. Self-introduction

   “Give a brief introduction about yourself, including your achievements and/or key projects/responsibilities you have taken up in the past?”

   TipKeep it crisp (2–3 minutes). Focus on academics, internships, and 1–2 impactful projects instead of narrating your entire resume.

2. Data science project experience

   “Could you elaborate upon a data science project in which you dealt with substantial datasets, offering an example that showcases your practical data analysis abilities? If you have used Python, which modules are you comfortable with?”

   TipPick one strong project (academic or internship). Talk about dataset size, challenges, tools (like Pandas, NumPy, Scikit-learn), and the results/impact.

3. SQL, visualization, and AI/ML skills

   “Have you worked on SQL, data visualization and/or AI/ML? If yes, please elaborate further on the projects relevant for the same.”

   TipThis is the chance to show your technical breadth. Even if your projects were small, show familiarity with SQL queries, visualization tools (Matplotlib, Plotly, Power BI, Tableau), and any ML models you’ve built.

4. Motivation and fit

   “What motivated you to apply for this role and why do you think you are a good fit for this role?”

   TipLink your skills and interests with Rystad’s work. For example, emphasize data-driven decision-making, interest in energy and market analytics, and how your previous experience prepared you for this role.

Interviews

There were two rounds of interviews after the video assessment: a technical round for Data Analysts (a case study round for Research Analysts), then a techno-managerial / HR round.

1. Technical round (Data Analyst role)

The round began with a short introduction, both from me and the interviewers, followed by a discussion of my previous experiences and projects.

Then came the technical evaluation, which focused on:

• Python: core concepts, data structures, Pandas operations, etc.
• SQL: joins, subqueries, GROUP BY, query optimization, etc.
• Machine Learning: basics of supervised vs. unsupervised learning, overfitting, evaluation metrics, time-series forecasting, etc.

A few aptitude and behavioral questions were also included:

• Guesstimates (e.g. “How many petrol pumps are there in Bangalore?”)
• “Why Rystad?”
• “What makes you a good fit for Rystad?”

TipBe ready with 1–2 well-prepared projects to discuss in detail. They often dive deep into your role, the dataset, the tools used, and the challenges you solved.

2. Techno-managerial / HR round

This was more of a conversational round. It started similarly, with introductions and a recap of past experiences, but the focus shifted towards communication skills, clarity of thought, and general interests and industry awareness.

The interviewer casually explored teamwork, how I approach problem-solving, and my broader career aspirations. It felt less like a grilling session and more like checking whether I would fit into Rystad’s work culture.

TipStay confident, be yourself, and keep your answers structured. This round is less about “right answers” and more about how you communicate and present yourself.

Resources

Here are some resources I used (and recommend):

You can also use any similar resources to practice further, and AI tools (ChatGPT, Gemini, etc.) to get detailed explanations of any concept you find tricky.