Top 22 BP Data Scientist Interview Questions + Guide in 2024

Headquartered in London, BP (formerly British Petroleum) is an oil and gas supermajor. This multinational company operates in all areas of the oil and gas industry, including extraction, distribution, and marketing. Despite recently investing in renewable sources, BP’s finances, for the most part, still rely on fossil fuels.

As a prospective data scientist at BP, you will be responsible for delivering statistical analysis, presenting potential areas for improvement, developing pricing models, and more. You may also occasionally travel as part of the job.

But now, as a candidate, you may be curious about the BP data scientist interview questions. For this reason, we’ve gathered info from interviewers and past interviewees in this article to help you gain a competitive edge.

BP embraces a structured interview procedure to assess candidates based on their applied skills and ability to handle particular situations—allowing you to express your communication and networking skills.

Staying true to the process, interviewers design technical and behavioral questions in a way that reckons your ability to perform essential functions of the role you’re applying for. Here is how it usually goes for the data scientist role at BP:

Application Process

If a data scientist position is open, you’ll either be approached by a recruiter or need to apply through the BP Career portal. A specialized talent acquisition team and the hiring manager will screen your application. The application form for the BP data scientist role often includes questions on analytics and programming.

An up-to-date CV, accurate application form, and proper technical answers can dramatically increase your odds of getting called back.

Telephone or Video Interview

If your application is accepted, your contact from the BP team will arrange a telephone or video interview to ascertain if you meet the minimum criteria for the role. Your responses during the application process will also be verified via this call.

Psychometric Tests

Depending on the role you’re applying for and the level of involvement required, you may need to undergo a psychometric test to demonstrate your personality and cognitive abilities. If relevant to your specific data scientist role, your talent acquisition contact will offer clear instructions on taking the test and answer any questions you may have.

Face-to-Face Interview

Success in the previous rounds will propel you to the final face-to-face round, conducted at a BP assessment center. There, you’ll have the opportunity to meet your hiring manager and talent acquisition contact, among other stakeholders. Multiple interviews will be conducted during this stage to further evaluate your behavioral competency and technical ability.

Pre-Employment Screening

If you have been successful in the face-to-face interviews, your recruiter will verbally contact you to make an offer. After you accept, a follow-up email will confirm the offer. Pre-employment screening tests are also performed before BP welcomes you as a data scientist.

BP usually asks questions about analytics and programming in their data scientist interviews. Here are a few recurring questions with sample answers to prepare yourself:

1. What are your three biggest strengths and weaknesses?

Your self-awareness, ability to reflect on your strengths and weaknesses, and compatibility with BP’s values and culture will be assessed through this question.

How to Answer

Identify three genuine strengths and three areas you need to improve. When discussing weaknesses, focus on how you’re actively working to address them.

Example

“Three strengths I’ve identified in myself include strong problem-solving skills, adaptability to new situations, and effective communication. On the flip side, I’m working on improving my time management, seeking feedback regularly, and enhancing my technical skills through continuous learning and development.”

2. What makes you a good fit for our company?

With this question, BP wants to know your unique qualities or experiences that align with their company values, culture, and goals.

How to Answer

Highlight your relevant skills, experiences, and passion for the industry. Connect these to specific aspects of BP’s mission, values, or projects.

Example

“I believe my extensive experience in renewable energy projects aligns well with BP’s commitment to sustainability. Additionally, my background in data analytics and problem-solving skills can contribute effectively to the company’s innovation initiatives, such as optimizing energy efficiency and reducing carbon footprint.”

3. Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?

This question evaluates your ability to go above and beyond in a project, showcasing your initiative, problem-solving skills, and determination as a data scientist.

How to Answer

Choose a specific example where you took the initiative, overcame challenges, and achieved outstanding results. Describe the actions you took and their impact.

Example

“During a data science project, I proactively identified inefficiencies in our methodology and proposed a new approach involving principal component analysis (PCA). Despite initial skepticism, I led the team in implementing the new method, resulting in a 20% increase in accuracy and a 30% reduction in processing time, exceeding the project’s expectations.”

4. Tell me about a time when your colleagues did not agree with your approach. What did you do to bring them into the conversation and address their concerns?

The interviewer at BP wants to evaluate your ability to handle conflicts diplomatically and collaboratively, ensuring effective communication and teamwork.

How to Answer

Respond by explaining a past disagreement and how you approached it respectfully, ultimately reaching a resolution that satisfied all parties involved.

Example

“During a strategy planning session, my colleagues disagreed with my proposed modeling approach. I actively listened to their concerns, provided evidence to support my viewpoint, and encouraged an open discussion. Through constructive dialogue, we identified common ground and adjusted the strategy to incorporate everyone’s input, resulting in a stronger and more cohesive plan.”

5. Give an example of when you resolved a conflict with a coworker.

This question evaluates your interpersonal skills and ability to resolve conflicts professionally, maintaining positive relationships and productivity in the workplace.

How to Answer

Share a specific example where you successfully mediated a conflict, demonstrating your empathy, communication skills, and ability to find mutually beneficial solutions.

Example

“I once had a disagreement with a team member over project priorities. Instead of escalating the tension, I initiated a one-on-one to understand their perspective. Through active listening and empathy, we identified underlying concerns and collaboratively devised a solution that balanced both our priorities. As a result, we resolved the conflict and strengthened our working relationship, leading to smoother collaboration in the future.”

6. You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

As a data scientist, your understanding of statistical hypothesis testing and the challenges associated with multiple comparisons will be evaluated with this question.

How to Answer

Mention how adjusting for multiple comparisons is crucial when conducting multiple t-tests. Consider using methods like Bonferroni correction or False Discovery Rate correction to mitigate the risk of Type I errors.

Example

“I would address the issue of multiple comparisons by applying appropriate correction methods such as Bonferroni or false discovery rate. These methods help control the increased probability of false positives when testing multiple hypotheses simultaneously, ensuring more reliable results.”

7. Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city. The company has been consistently adding new users in the city from January to March. Why might the average number of comments per user be decreasing, and what metrics would you look into?

The interviewer at BP will evaluate your ability as a data scientist to identify potential reasons for a decrease in a specific metric despite overall growth and suggest relevant metrics to investigate further.

How to Answer

Discuss potential reasons for decreasing comments per user. Also, mention relevant metrics to investigate the issue further.

Example

“I would explore potential reasons for the decrease, such as changes in user behavior due to seasonal factors or shifts in content relevance. Additionally, I would examine metrics like engagement rates, user activity patterns, and demographic changes to gain insights into the underlying causes.”

8. Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value. How would you assess the validity of the result?

This question tests your understanding of statistical significance and the validity of experimental results, which are necessary skills for a data scientist at BP.

How to Answer

Assess the result by considering factors such as sample size, effect size, experimental design, and potential biases. Verify that the observed effect is practically significant and not merely statistically significant.

Example

“To assess the validity of the result, I would consider factors such as the sample size, effect size, and experimental design. Additionally, I would evaluate the presence of any biases or confounding variables that could influence the outcome. It’s essential to ensure that the observed effect is not only statistically significant but also practically meaningful.”

9. Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently commissioned a study that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline. What factors could have biased this result, and what would you look into?

Your critical thinking skills regarding potential biases in study results and the factors to investigate for bias mitigation will be evaluated through this question.

How to Answer

Identify potential biases such as selection, measurement, or funding. Investigate factors such as study design, data collection methods, and the independence of the study sponsor. Additionally, discuss specific strategies to address each potential bias.

Example

“I would scrutinize the study design and data collection methods to identify potential biases such as selection bias, where certain airlines or flights may have been chosen selectively, skewing the results. Moreover, measurement bias could also occur if the boarding times were recorded inconsistently or inaccurately across airlines.

To mitigate selection bias, it would be beneficial to ensure random selection of flights across different airlines and consider factors like flight duration and passenger load. To address measurement bias, implementing standardized procedures for recording boarding times and verifying data accuracy through independent verification could be useful. By meticulously addressing these potential biases and implementing appropriate corrective measures, we can enhance the reliability and validity of the study results.”

10. What are the differences between Lasso and ridge regressions?

Your understanding of regularization techniques in linear regression and the differences between Lasso and ridge regression will be evaluated at the BP interview as a data scientist candidate.

How to Answer

Explain that both methods introduce penalties to the regression coefficients to prevent overfitting. Lasso uses an L1 penalty, which can result in sparse coefficients, while ridge uses an L2 penalty, which shrinks coefficients toward zero without necessarily setting them to zero. Provide examples to illustrate the impact of these penalties on the regression coefficients.

Example

“The key distinction between Lasso and ridge regressions lies in the type of penalty they impose on the regression coefficients. Lasso employs an L1 penalty, which introduces sparsity by penalizing the absolute values of the coefficients. Consequently, Lasso can force some coefficients to be exactly zero, effectively performing feature selection by eliminating less relevant variables from the model. On the other hand, ridge regression applies an L2 penalty, which penalizes the squared magnitudes of the coefficients. While ridge regression also shrinks the coefficients toward zero, it rarely sets them exactly to zero, allowing all features to contribute to the model.”

11. How would you approach a situation where your data has a high number of outliers?

The BP interviewer will assess your ability to deal with data anomalies and maintain data integrity, which is crucial for accurate analysis in the energy industry.

How to Answer

Consider employing robust statistical techniques such as trimming or winsorizing to mitigate the impact of outliers without entirely removing them. You may also explore using outlier detection algorithms to identify and understand the nature of outliers before deciding on an appropriate treatment strategy.

Example

“I would start by visually inspecting the data distribution and then use statistical methods like z-scores or interquartile range (IQR) to identify outliers. Once identified, I’d assess the impact of outliers on the analysis and consider using techniques like trimming or winsorizing to mitigate their influence while preserving the integrity of the dataset. Additionally, employing machine learning algorithms such as isolation forests or robust regression models could help identify and handle outliers more effectively.”

12. How would you identify unusual patterns in power grid sensor data that might indicate potential equipment failure?

With this question, the interviewer will evaluate your ability to detect anomalies in sensor data, which is critical for proactive maintenance and preventing potential equipment failures in the power grid.

How to Answer

Explore techniques such as time series analysis, anomaly detection algorithms (e.g., isolation forests, autoencoders), or machine learning models trained on historical data to identify patterns deviating from normal operation. Additionally, domain knowledge of power grid systems can aid in understanding typical operations and recognizing abnormal behavior.

Example

“I would start by preprocessing the sensor data and then apply time series analysis techniques like moving averages or exponential smoothing to detect trends and seasonal patterns. Also, I would employ anomaly detection algorithms such as isolation forests or autoencoders to flag data points that significantly deviate from expected behavior. Incorporating domain knowledge about power grid systems would help in distinguishing between benign fluctuations and potential indicators of equipment failure.”

13. Discuss how linear programming or other optimization techniques could be used for tasks like power plant scheduling or energy resource allocation.

Your understanding of optimization techniques and their application in operations within the power industry, which is crucial for efficient resource usage and cost reduction, will be assessed in your BP data scientist interview.

How to Answer

Explain how linear programming models can be formulated to optimize power plant scheduling by considering factors like demand, generation capacity, and operational constraints. Additionally, discuss other optimization techniques, such as mixed-integer programming or metaheuristic algorithms, that can address more complex scheduling and resource allocation problems in the energy sector.

Example

“Linear programming can be used for power plant scheduling by formulating an objective function to minimize costs or maximize revenue while satisfying constraints such as demand requirements and operational limits. Mixed-integer programming extends this approach by allowing decision variables to take integer values, enabling the modeling of discrete decisions like unit commitment. Furthermore, metaheuristic algorithms like genetic algorithms or simulated annealing can efficiently explore solution spaces for complex optimization problems, offering flexibility in addressing various operational challenges in the energy industry.”

14. How would you ensure your data science models and analyses are scalable to handle the potentially massive datasets in the power industry?

This question evaluates your understanding of scalability concerns in data science and your ability to design solutions that can efficiently handle large volumes of data prevalent in the power sector.

How to Answer

Discuss strategies such as distributed computing frameworks (e.g., Apache Spark), data partitioning techniques, and parallel processing to handle massive datasets effectively. Additionally, emphasize the importance of optimizing algorithms and using cloud-based solutions for elastic scalability.

Example

“To ensure scalability, I would use distributed computing frameworks like Apache Spark to parallelize data processing tasks across multiple nodes, enabling efficient handling of large datasets. Data partitioning techniques such as hash partitioning or range partitioning can further enhance parallelism by distributing data evenly among cluster nodes. Additionally, I would optimize algorithms for parallel execution and explore cloud-based solutions like AWS or Google Cloud Platform, which offer elastic scalability to accommodate fluctuating data volumes and computational demands in the power industry.”

15. How would you handle missing data in sensor readings from power plants, especially if the missingness is not random?

The technical interviewers at BP will check your ability to address missing data issues, which are common in sensor data from power plants, particularly when the missingness is not random, potentially indicating underlying system issues.

How to Answer

Propose techniques such as imputation methods tailored for non-random missing data patterns, such as regression imputation or interpolation based on neighboring sensor readings. Also, consider domain knowledge to identify potential reasons for missing data and incorporate contextual information into the imputation process.

Example

“If the missingness in sensor readings is not random, I would first investigate potential reasons for data gaps, such as sensor malfunction or maintenance activities. Based on domain knowledge and understanding of power plant operations, I would design customized imputation strategies, such as regression imputation using correlated sensor variables or interpolation based on temporal trends and neighboring sensor measurements. Also, I would monitor imputed values for consistency with known system behavior and iteratively refine the imputation approach if necessary to ensure the accuracy of downstream analyses.”

16. Discuss the potential security risks associated with data collection and analysis in the power industry, and how data anonymization or differential privacy techniques could be used to mitigate them.

Your understanding of security risks as a data scientist working at BP and your familiarity with techniques for safeguarding data privacy within the power industry will be evaluated with this question.

How to Answer

Discuss common security risks such as data breaches, unauthorized access, and data manipulation. Then, explain how techniques like data anonymization or differential privacy can help reduce these risks by protecting sensitive information while still allowing meaningful analysis.

Example

“In the power industry, data security is paramount due to the sensitivity of information and potential impacts of breaches. Common risks include unauthorized access to critical infrastructure data and the possibility of manipulation leading to system vulnerabilities. To reduce these risks, techniques like data anonymization or differential privacy can be employed. Data anonymization involves removing personally identifiable information from datasets while still preserving the integrity of the data for analysis. On the other hand, differential privacy adds noise to query results, ensuring that individual data points cannot be traced back to specific individuals. By implementing these techniques, BP can safeguard its data assets while deriving valuable insights for decision-making.”

17. In the context of energy trading, how would you use data analytics to optimize trading strategies and mitigate risks?

This question evaluates your ability to apply data science and data analysis techniques in the energy trading domain to optimize strategies and manage risks effectively.

How to Answer

Outline the process of leveraging data analytics to analyze market trends, forecast demand, and identify trading opportunities. Discuss techniques such as machine learning algorithms for predictive modeling and risk assessment to develop robust trading strategies.

Example

“To optimize energy trading strategies and mitigate risks, data analytics plays a crucial role. By analyzing historical market data, including supply and demand trends, price fluctuations, and geopolitical factors, we can identify patterns and correlations that inform trading decisions. Using machine learning algorithms such as regression, time series analysis, and reinforcement learning enables the development of predictive models for price forecasting and risk assessment. These models help identify profitable trading opportunities while managing exposure to market volatility and other risks associated with energy trading.”

18. BP operates in various regions with different regulatory environments. How would you adjust your analytical approach when dealing with data from regions with different regulations?

As a data scientist candidate, your adaptability and understanding of how regulatory differences across regions impact data analysis approaches will be assessed in the BP interview.

How to Answer

Explain how regulatory variations can affect data availability, privacy requirements, and compliance obligations. Discuss the importance of tailoring analytical approaches to adhere to specific regulations while ensuring consistency and accuracy in data analysis.

Example

“When dealing with data from regions with different regulatory environments, it’s essential to adapt the analytical approach accordingly. Regulatory variations may impact data collection, storage, and usage practices due to differences in privacy laws, reporting requirements, and industry standards. As such, we must carefully consider compliance obligations and privacy concerns when designing analytical frameworks. This may involve implementing region-specific data anonymization techniques, ensuring GDPR compliance in European regions, or adhering to local data protection regulations. By customizing analytical approaches to meet regulatory requirements while maintaining analytical integrity, BP can effectively navigate diverse regulatory landscapes while leveraging data for informed decision-making.”

19. BP aims to transition toward renewable energy sources. How would you leverage data analytics to assess the feasibility and impact of renewable energy projects?

This question evaluates your ability to apply data analytics in assessing the feasibility and impact of renewable energy projects at BP.

How to Answer

Discuss how data analytics can be used to analyze factors such as resource availability, environmental impact, cost-effectiveness, and regulatory constraints to evaluate the feasibility of renewable energy projects. Highlight the role of predictive modeling and scenario analysis in assessing long-term impacts and optimizing project outcomes.

Example

“To assess the feasibility and impact of renewable energy projects, data analytics offers valuable insights across various dimensions. By analyzing geospatial data, weather patterns, and historical energy production data, we can evaluate the availability and reliability of renewable resources such as solar and wind energy. Additionally, data analytics enables the assessment of environmental impact, considering factors like carbon emissions, land use, and biodiversity conservation. Cost-effectiveness analysis incorporating factors such as capital expenditure, operational expenses, and government incentives further informs decision-making. By using data analytics in this manner, BP can make informed decisions regarding the transition to renewable energy sources, ensuring sustainability and maximizing value.”

20. Explain how you would design and implement a predictive maintenance model for BP’s power generation infrastructure. What data would you use, and how would you evaluate the model’s performance?

Through this question, the interviewer will check your ability to design and implement predictive maintenance models for power generation infrastructure, considering relevant data sources and evaluation metrics.

How to Answer

Outline the process of data collection, feature engineering, model selection, and deployment for predictive maintenance. Discuss the types of data utilized, such as equipment sensor data, maintenance logs, and environmental conditions. Explain evaluation metrics like accuracy, precision, recall, and F1 score for assessing model performance.

Example

“To design and implement a predictive maintenance model for BP’s power generation infrastructure, I would first gather relevant data sources, including equipment sensor data capturing operational parameters, historical maintenance logs detailing past failures, and environmental conditions affecting equipment performance. I would use feature engineering techniques to extract meaningful features from raw data, such as trend analysis, anomaly detection, and time-series decomposition. Model selection would involve choosing appropriate algorithms such as random forest, gradient boosting, or long short-term memory (LSTM) networks for time-series data. The model would be trained on historical data to predict equipment failures or maintenance needs proactively.”

21. Let’s say you’re given a huge 100 GB log file. You want to be able to count how many lines are in the file. How would you count the total number of lines in the file in Python?

As a data engineer candidate, your ability to efficiently handle large-scale data processing tasks and optimize resource usage will be evaluated in the interview.

How to Answer

Describe how you would approach the problem of counting lines in an extremely large file without exhausting system memory. Emphasize the importance of streaming the file or processing it in chunks to avoid loading the entire file into memory, which is crucial for handling large datasets.

Example

“When working with extremely large files, such as a 100 GB log file, it’s critical to process the data in a way that doesn’t overwhelm system resources. One effective approach is to stream the file line by line, which allows us to count each line without holding the entire file in memory. By using a simple loop within a context manager, we can efficiently iterate through the file and tally the lines. Alternatively, for cases where newline characters might not be easily parsed, we can read the file in manageable chunks and count the line breaks within those chunks. This method ensures that we handle the file efficiently, preventing memory issues and allowing for accurate line counting regardless of the file’s size.”

22. What do the AR and MA components of ARIMA models refer to? How do you determine their order?

Your knowledge of the AR and MA components in ARIMA models and how to determine their order will be tested during the data science interview.

How to Answer

Describe the roles of the AR and MA components in modeling time series data. The AR (autoregression) component captures the relationship between a current data point and its previous values, while the MA (moving average) component models the dependency of a data point on past errors. Discuss how the orders of these components, represented by p for AR and q for MA, are determined through techniques like grid search, often using metrics like the Akaike Information Criterion (AIC) to identify the optimal model.

Example

“The ARIMA model combines both autoregression (AR) and moving average (MA) components to effectively model time series data. The AR component (order p) captures the influence of past values on the current data point, where each previous value contributes to the prediction. The MA component (order q) accounts for the impact of past errors or residuals on the current data point, allowing the model to adjust for fluctuations not explained by the AR component alone. To determine the optimal values for p and q, we typically perform a grid search over a defined range of potential values, evaluating models using a criterion like AIC to select the best-performing combination. This approach ensures that the chosen ARIMA model balances complexity with predictive accuracy.”

Memorizing canned answers and fumbling to find your way through critical questions will not land you the data scientist role. However, being informed about the latest industry trends and trained in modern data science techniques can increase your chance of cracking the interview.

The interviewers at BP will expect a certain extent of problem-solving and analytical skills from you, which can be developed with practice. Here’s how you can begin preparing for the data scientist interview at BP:

Understanding BP’s Operations

Research BP’s main business operations, including its oil and gas exploration, production, refining, and marketing strategies. Learn about its culture, values, and business goals to better align your answers to the data science behavioral questions.

Also, research the challenges BP may be facing (e.g., reduced use of oil and gas), mitigation strategies as a data scientist (e.g., investment opportunities in alternative sources), and latest market trends (e.g., hydrogen fuel).

Mastering Data Science Fundamentals

Frequently brushing up on your data science fundamentals, including statistics, data analytics, and machine learning, will help you confidently answer the data science case study questions. However, in the commotion of analytical domains, don’t forget about programming languages, including Python and SQL.

To develop a better profile, use our datasets for data science to build projects and showcase your analytical prowess. If you haven’t yet, here’s how to create a data science project.

Developing Communication Skills

Along with verbal communication skills, analytical communication skills are also paramount for a data scientist candidate at BP. Your ability to convey ideas and results effectively (both visually and verbally) will be evaluated during the interview and celebrated as an employee if you get the job.

Attending Mock Interviews

Preparedness peaks when you can confidently answer the questions coming from your interviewer and get your questions answered regarding the data scientist role at BP. Peer-to-peer mock interviews to further refine your communication skills can increase your interview prowess. Engaging with other candidates will boost your confidence and give you practice in refining your answers.

Preparing Interview Questions

Be prepared with model answers to the most probable types of data science interview questions, which we’ve made available. Consider going through data science projects to better understand the practical scenarios you may work on at BP. Also, remember to access our resource with common Python questions for data science candidates.

Where can I find salary information for BP’s data scientist role?

Unfortunately, we don’t have enough data samples to verify the base salary and total compensation for the BP data scientist role. However, you may check the industry standard compensations from our data scientist salary guide.

Where can I read about past interview experiences for the BP data scientist role?

We have an extensive Slack community that thrives on helping candidates with information regarding the interview processes in different companies, including BP. You can also contribute by sharing your experience after the interview.

Does Interview Query have job postings for the BP data scientist role?

Yes. Our job portal offers the latest openings, which you can apply to directly. However, job openings in specific positions are subject to availability.

BP data scientist interview questions usually include questions about analytics, statistics, machine learning, and programming languages. A couple of behavioral questions tangential to your experiences may also be asked during the interview rounds. To approach the interview confidently, develop your communication and analytical skills, participate in mock interviews, and solve as many interview questions as you can.

Moreover, consider exploring our BP main interview guide and keep your options open with BP data analytics and data engineer positions.

All your preparation can only take you so far. It’s important to be genuine during the interview. We extend our heartfelt wishes for your success and look forward to hearing about your experience interviewing for the data scientist position at BP!