Harnham Research Scientist Interview Questions + Guide in 2025

Harnham is a leading recruitment firm specializing in data and analytics, connecting top-tier talent with innovative companies across various sectors.

The Research Scientist role at Harnham involves leveraging advanced statistical analysis and machine learning techniques to drive impactful research outcomes. Key responsibilities include managing data-driven projects, conducting causal analyses, and developing robust data visualizations to communicate findings effectively. Candidates should have a strong proficiency in Python and R, along with a deep understanding of global health challenges, particularly regarding women's reproductive health. Ideal candidates are dynamic, self-motivated individuals with a passion for research and a track record of working with complex datasets, especially in low-income contexts.

This guide will help you prepare effectively for your interview by providing insights into the specific skills and experiences that are highly valued in this role, enabling you to demonstrate your suitability and stand out among other candidates.

The interview process for a Research Scientist role at Harnham is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several key stages:

1. Initial Screening

The process begins with an initial screening call, usually conducted by an in-house recruiter. This call lasts around 15-30 minutes and focuses on your background, motivations for applying, and a brief overview of your relevant skills. The recruiter will also gauge your interest in the role and the company culture, ensuring alignment with Harnham's values.

2. Technical Assessment

Following the initial screening, candidates may be required to complete a technical assessment. This could involve a take-home assignment or a live coding session where you demonstrate your proficiency in relevant programming languages such as Python or R. You may be asked to analyze a dataset, perform statistical modeling, or create visualizations that effectively communicate your findings. This stage is crucial for showcasing your analytical skills and ability to handle complex data.

3. Presentation Round

Candidates who successfully pass the technical assessment will be invited to a presentation round. In this stage, you will prepare a presentation based on a given topic or project relevant to the role. This could involve discussing your previous research, methodologies used, and insights gained. You will present your findings to a panel that may include team members and managers, who will evaluate your communication skills, depth of knowledge, and ability to engage with both technical and non-technical stakeholders.

4. Final Interview

The final interview typically involves a one-on-one or panel interview with senior team members or management. This stage focuses on behavioral questions and situational scenarios to assess your problem-solving abilities, teamwork, and how you handle challenges. Expect questions that explore your past experiences, your approach to research, and how you would contribute to Harnham's projects and goals.

Throughout the process, candidates should be prepared to discuss their experience with statistical analysis, causal analysis, and any relevant projects they have worked on, particularly those involving large datasets or survey data.

As you prepare for your interview, consider the types of questions that may arise in each of these stages.

Here are some tips to help you excel in your interview.

Understand the Company’s Mission and Values

Harnham is focused on leveraging data science to drive impactful change, particularly in areas like global health and financial technology. Familiarize yourself with their recent projects and initiatives, especially those related to reproductive health and AI advancements. This knowledge will not only demonstrate your genuine interest in the company but also help you align your responses with their mission during the interview.

Prepare for a Multi-Step Interview Process

The interview process at Harnham can involve multiple stages, including initial calls with recruiters and presentations to team members. Be ready to articulate your experience clearly and concisely. If you are asked to present, ensure your presentation is well-structured, visually engaging, and tailored to the audience's expertise. Practice explaining complex concepts in simple terms, as you may need to communicate with both technical and non-technical stakeholders.

Showcase Your Technical Proficiency

Given the emphasis on statistical analysis, causal analysis, and programming skills (especially in R and Python), be prepared to discuss your technical expertise in detail. Highlight specific projects where you utilized these skills, focusing on the methodologies you employed and the outcomes achieved. If possible, bring examples of your work, such as visualizations or code snippets, to illustrate your capabilities.

Emphasize Collaboration and Communication Skills

Harnham values teamwork and effective communication. Be prepared to discuss how you have successfully collaborated with cross-functional teams in the past. Share examples of how you have communicated complex data findings to diverse audiences, ensuring that your insights were understood and actionable. This will demonstrate your ability to thrive in a collaborative environment.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on past experiences where you faced obstacles, how you approached them, and what you learned from those situations. This will help you convey your resilience and adaptability.

Stay Authentic and Engaged

Throughout the interview, maintain a personable demeanor. Show enthusiasm for the role and the company, and don’t hesitate to ask insightful questions about the team dynamics, company culture, and future projects. This not only demonstrates your interest but also helps you gauge if Harnham is the right fit for you.

Follow Up Professionally

After the interview, send a thoughtful thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the role and briefly mention a key point from the conversation that resonated with you. This will leave a positive impression and keep you top of mind as they make their decision.

By following these tailored tips, you can position yourself as a strong candidate for the Research Scientist role at Harnham. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Research Scientist role at Harnham. The interview process will likely focus on your technical expertise in machine learning, statistical analysis, and your ability to communicate complex findings effectively. Be prepared to discuss your experience with data analysis, project management, and your understanding of the specific challenges in the health tech or AI sectors.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category. Highlight the importance of each in real-world applications.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as using regression for predicting house prices. In contrast, unsupervised learning deals with unlabeled data, where the model identifies patterns or groupings, like clustering customers based on purchasing behavior.”

2. Describe a machine learning project you have led. What were the challenges and outcomes?

This question assesses your practical experience and problem-solving skills.

How to Answer

Discuss the project scope, your role, the challenges faced, and how you overcame them. Emphasize the impact of the project.

Example

“I led a project to develop a predictive model for patient readmission rates. The main challenge was dealing with missing data, which I addressed by implementing imputation techniques. The model ultimately reduced readmissions by 15%, significantly improving patient care.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model assessment techniques.

How to Answer

Mention various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. Explain when to use each metric.

Example

“I evaluate model performance using accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall. For instance, in a medical diagnosis model, I focus on recall to minimize false negatives, ensuring that most patients with the condition are identified.”

4. What is overfitting, and how can it be prevented?

This question gauges your understanding of model training and generalization.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor performance on unseen data. I prevent it by using techniques like cross-validation to ensure the model generalizes well and applying regularization methods to penalize overly complex models.”

Statistics & Probability

1. Explain the concept of p-value in hypothesis testing.

This question assesses your statistical knowledge relevant to data analysis.

How to Answer

Define p-value and its significance in hypothesis testing, including its interpretation.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that the observed effect is statistically significant.”

2. How would you handle missing data in a dataset?

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. If it’s minimal, I might use mean imputation. For larger gaps, I prefer multiple imputation techniques to maintain the dataset's integrity and avoid bias.”

3. Can you describe a time when you used statistical analysis to solve a problem?

This question looks for practical application of your statistical knowledge.

How to Answer

Provide a specific example, detailing the problem, the statistical methods used, and the outcome.

Example

“I analyzed survey data to identify factors affecting contraceptive use in low-income countries. By applying logistic regression, I found that education level significantly influenced usage rates, which informed our outreach strategies and improved program effectiveness.”

4. What is the Central Limit Theorem, and why is it important?

This question tests your foundational knowledge in statistics.

How to Answer

Explain the theorem and its implications for statistical inference.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about population parameters based on sample statistics.”

Data Visualization

1. How do you choose the right visualization for your data?

This question assesses your ability to communicate data insights effectively.

How to Answer

Discuss factors influencing your choice of visualization, such as the data type, audience, and the message you want to convey.

Example

“I choose visualizations based on the data type and the story I want to tell. For categorical data, I might use bar charts, while for trends over time, line graphs are more effective. I always consider the audience to ensure clarity and engagement.”

2. Can you provide an example of a visualization you created that had a significant impact?

This question looks for evidence of your ability to create impactful visualizations.

How to Answer

Describe the visualization, the data it represented, and the impact it had on decision-making.

Example

“I created a dashboard visualizing maternal health statistics across different regions. By using heat maps, stakeholders quickly identified areas needing urgent intervention, leading to targeted resource allocation and improved health outcomes.”

3. What tools do you use for data visualization, and why?

This question evaluates your familiarity with visualization tools.

How to Answer

Mention specific tools you are proficient in and their advantages.

Example

“I primarily use Tableau for its user-friendly interface and ability to handle large datasets. For more complex visualizations, I use Python libraries like Matplotlib and Seaborn, which offer greater flexibility and customization.”

4. How do you ensure your visualizations are accessible to all stakeholders?

This question assesses your awareness of accessibility in data presentation.

How to Answer

Discuss strategies for making visualizations accessible, such as color choices and providing alternative text.

Example

“I ensure accessibility by using color palettes that are colorblind-friendly and providing alternative text descriptions for key visualizations. I also consider the layout and clarity of labels to make the information easily digestible for all stakeholders.”