The International Rescue Committee (IRC) responds to the world's worst humanitarian crises, helping to restore health, safety, education, economic wellbeing, and power to people devastated by conflict and disaster. Founded in 1933 at the call of Albert Einstein, the IRC is one of the world's largest international humanitarian non-governmental organizations (INGO), operating in more than 50 countries and over 25 U.S. cities.

As a Data Scientist in the Fundraising sector at IRC, you'll collaborate closely with the Associate Director, Mass Marketing Analytics, to provide critical analysis and recommendations that drive income growth. Working remotely, you'll undertake responsibilities like developing advanced analytics products, building data models, and monitoring key performance indicators. This is an excellent opportunity for a data-driven professional seeking to make a substantial impact on IRC's global mission.

Explore more about IRC's Data Scientist position through the comprehensive resources provided by Interview Query for your interview preparation journey.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining the International Rescue Committee as a Global Data Scientist. Whether you were contacted by an IRC recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the IRC Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be part of the screening process.

In some cases, the IRC Global Data Scientist hiring manager may be present during the screening round to answer your queries about the role and the company itself. They may also engage in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Global Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around IRC's data systems, ETL pipelines, and SQL queries.

In the case of data science roles, take-home assignments regarding product metrics, analytics, and data visualization could be included. Apart from these, your proficiency in hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, real-scenario problems and case studies may also be assigned.

Onsite Interview Rounds

Following a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day within the IRC team. Your technical prowess, including programming and machine learning modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Global Data Scientist role at IRC.

Quick Tips For IRC Global Data Scientist Interviews

Here are a few tips for acing your IRC Global Data Scientist interview:

• Understand IRC's Mission: IRC is committed to humanitarian services, and understanding their core mission and values will greatly help in framing your answers. Be ready to provide insights on how your past work aligns with IRC's objectives.
• Be Data-Driven: Data-driven decision-making is crucial for IRC's operations. Brush up on your knowledge of statistics, data modeling, and data visualization, as these will be central to the role.
• Showcase Soft Skills: As much as your technical capabilities matter, IRC values collaboration and communication skills. Prepare to discuss how you’ve worked with cross-functional teams and can communicate complex data insights to non-technical stakeholders.

Typically, interviews at International Rescue Committee vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test multiple changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you set up this test?

• Why are job applications decreasing despite a steady number of job postings? You observe that the number of job postings per day has remained constant, but the number of applicants has been decreasing. What could be the reasons for this trend?

• Can unbalanced sample sizes in an A/B test result in bias towards the smaller group? You need to analyze an A/B test where one variant has a sample size of 50K users and the other has 200K users. Can the unbalanced sizes lead to bias towards the smaller group?

• How can you check if assignment to A/B test buckets was truly random? In an A/B test, how would you verify that the assignment to different buckets was genuinely random?

• How would you assess the validity of an A/B test result with a 0.04 p-value? Your company is running a standard control and variant A/B test to increase conversion rates on a landing page. The PM finds a p-value of 0.04. How would you assess the validity of this result?

• What are the key differences between classification models and regression models? Explain the primary distinctions between classification and regression models, focusing on their objectives, output types, and typical use cases.

• What happens when you run logistic regression on perfectly linearly separable data? Describe the behavior and potential issues of logistic regression when applied to a dataset that is perfectly linearly separable.

• When would you use a bagging algorithm versus a boosting algorithm? Compare the use cases for bagging and boosting algorithms, providing examples of the tradeoffs between the two approaches.

• What’s the difference between Lasso and Ridge Regression? Explain the differences between Lasso and Ridge Regression, focusing on their regularization techniques and effects on model coefficients.

• How does random forest generate the forest, and why use it over logistic regression? Describe the process by which random forest generates its ensemble of decision trees and discuss the advantages of using random forest over logistic regression in certain scenarios.

• What are time series models and why are they needed over simpler regression models? Explain what time series models are and discuss why they are necessary when simpler regression models might not suffice.

• What happens when you run logistic regression on perfectly linearly separable data? Given a dataset that is perfectly linearly separable, describe the outcome of running logistic regression on it.

• What is the probability of rolling at least one 3 with 2 dice? You are playing a dice game with 2 dice. Calculate the probability of rolling at least one 3. Also, generalize the probability for (N) dice.

• Can unbalanced sample sizes in an AB test result in bias towards the smaller group? Analyze an AB test where one variant has 50K users and the other has 200K users. Determine if the unbalanced sample sizes could bias the results towards the smaller group.

• What happens to the target metric after applying a new UI that won by 5% in an AB test? You tested a new UI to increase conversion rates, and the test variant won by 5%. Predict the impact on the target metric after applying the new UI to all users, assuming no novelty effect.

What is the average salary for a Data Scientist at International Rescue Committee?

Q: What are the main responsibilities of a Global Data Scientist at IRC? The Global Data Scientist at IRC will be responsible for providing analysis and recommendations that underpin the Mass Marketing Team's data-based approach to growing income at IRC. This includes problem definition, data cleaning, crafting recommendations, data visualization, predictive modeling, monitoring trends, and acting as a technical leader within the analytics team.

Q: What skills and experience are required for the Data Scientist position? To qualify for the Global Data Scientist role, candidates need at least 4 years of experience in data science or statistics, proficiency with SQL and relational databases, and strong data visualization and communication skills. Experience in building data science models, applying advanced statistical techniques, and proficiency in R, Python, or Stata are also required.

Q: What is the compensation for the Data Scientist position at IRC? The compensation for the Data Scientist role ranges from USD 100,000.00 to 110,000.00 annually. The exact offer will be calibrated based on the work location and the individual candidate’s experience and skills relative to the job requirements.

Q: What kind of work environment and benefits can I expect at IRC? The work environment at IRC is a standard office setting, with some international travel required. Full-time employees are eligible for comprehensive benefits including medical, dental, and vision insurance, a 403b retirement savings plan, paid holidays, and paid time off. IRC is committed to providing an enabling environment for gender equality and offers benefits like parental leave and gender-sensitive security protocols.

Q: How can I prepare for an interview for the Data Scientist position at IRC? To prepare for an interview at IRC, research the organization and its mission thoroughly. Practice common interview questions, particularly those related to data analysis and visualization. Use Interview Query to sharpen your technical skills and get familiar with the types of advanced statistical techniques and data science models commonly used in the field.

Excited about making a difference through data science? The International Rescue Committee (IRC) is the place for you. Serving millions in humanitarian crises, IRC seeks a Global Data Scientist to drive strategic growth and impact through data-driven insights and advanced analytics. With an ethos grounded in integrity, accountability, and equality, this role offers a collaborative and multi-cultural environment.

For more insights about the company, check out our main International Rescue Committee Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for roles like Global Data Scientist and Analytics Engineer, where you can learn more about IRC’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every interview question and challenge.

Check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!