The fund for public health in new york, inc. Data Analyst Interview Guide

Getting ready for a Data Analyst interview at The Fund for Public Health in New York, Inc.? The Fund for Public Health in New York, Inc. Data Analyst interview process typically spans multiple question topics and evaluates skills in areas like public health data analytics, technical proficiency in SQL and SAS, epidemiological metrics, and the ability to communicate complex insights to diverse audiences. Interview preparation is especially important for this role, as Data Analysts here are expected to work with large, sometimes messy datasets from various sources, translate findings into actionable recommendations for community health initiatives, and adapt their communication for both technical and non-technical stakeholders.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Analyst positions at The Fund for Public Health in New York, Inc.
• Gain insights into The Fund for Public Health in New York, Inc.’s Data Analyst interview structure and process.
• Practice real Data Analyst interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the Data Analyst interview process at The Fund for Public Health in New York, Inc., along with sample questions and preparation tips tailored to help you succeed.

The Fund for Public Health in New York City (FPHNYC) is a 501(c)(3) non-profit organization dedicated to improving the health and well-being of all New Yorkers. FPHNYC partners with government agencies and the private sector to implement programs that address urgent public health challenges, promote innovative health solutions, and educate the public on health protection. Through its initiatives, the organization advances community health, supports health care enhancements, and works to reduce health disparities across the city. As a Data Analyst, you will help harness data to drive impactful public health decisions and program effectiveness.

As a Data Analyst at The Fund for Public Health in New York, Inc., you will be responsible for collecting, managing, and analyzing public health data to support various health initiatives and programs across New York City. You will work closely with program managers, epidemiologists, and other stakeholders to interpret data trends, generate reports, and provide actionable insights that inform policy decisions and program strategies. Typical tasks include designing data collection tools, ensuring data quality, and presenting findings through visualizations and presentations. This role plays a key part in advancing public health outcomes by enabling data-driven decision-making and supporting the organization’s mission to improve community health and well-being.

2.1 Stage 1: Application & Resume Review

The process begins with an initial screening of applications and resumes to assess candidates’ experience in public health data analysis, statistical programming (such as SAS and SQL), epidemiological concepts (e.g., risk ratios, odds ratios), and data cleaning experience. Resumes that highlight experience with large and complex datasets, data visualization, and working with multidisciplinary teams will stand out. Preparation at this stage should focus on tailoring your resume to showcase relevant quantitative, analytical, and communication skills, as well as any experience working with public sector or health-related data.

2.2 Stage 2: Recruiter Screen

This stage typically involves a brief phone call (20–30 minutes) with a recruiter or HR representative. The goal is to verify your interest in the organization, discuss your background, and assess your communication skills and alignment with the organization’s public health mission. Expect questions about your motivation, your understanding of the organization’s goals, and your general approach to data-driven problem-solving. Preparation should include researching the organization’s recent projects and being ready to articulate your interest in public health analytics.

2.3 Stage 3: Technical/Case/Skills Round

The technical interview is usually conducted by a panel of data analysts, epidemiologists, or team leads. It often includes both live technical questions and a take-home skills assessment. You may be asked to demonstrate your proficiency in statistical analysis (e.g., calculating risk ratios, odds ratios), data cleaning, and data wrangling using tools like SAS or SQL. The take-home assignment, typically due within 48 hours, will test your ability to analyze real-world datasets, draw actionable insights, and communicate findings clearly. To prepare, review core statistical concepts, practice writing queries, and be ready to explain your analytical process and reasoning.

2.4 Stage 4: Behavioral Interview

Behavioral interviews are designed to evaluate your collaboration skills, adaptability, and ability to communicate complex data insights to non-technical stakeholders. Interviewers may ask about your previous experiences working in cross-functional teams, overcoming challenges in data projects, and making data-driven recommendations accessible to diverse audiences. To prepare, develop concise stories that demonstrate leadership, teamwork, and your approach to demystifying technical concepts for broader impact.

2.5 Stage 5: Final/Onsite Round

The final stage often involves a panel or group interview with key team members, supervisors, or organizational leadership. This round may revisit technical topics and dive deeper into your problem-solving approach, your fit with the organization’s mission, and your ability to handle ambiguous data challenges. You may also be asked to present your take-home assignment or walk through a past project, emphasizing both technical rigor and clarity of communication. Preparation should include reviewing your previous work, anticipating follow-up questions, and practicing articulating your thought process under pressure.

2.6 Stage 6: Offer & Negotiation

Once interviews are complete, successful candidates are contacted for references and may receive an offer contingent on background checks and reference verification. The negotiation process covers compensation, start date, and any role-specific details. Preparation should involve researching typical compensation for similar roles in the public health sector and considering your priorities regarding benefits and work-life balance.

2.7 Average Timeline

The typical interview process for a Data Analyst at the Fund for Public Health in New York, Inc. spans between three to six weeks, though timelines can vary. Fast-track candidates may move through the process in as little as two weeks, especially if there is an urgent project need or strong alignment with organizational priorities. However, it is not uncommon for there to be significant delays between stages, particularly between team interviews and final decisions, due to internal coordination or shifting project needs.

Next, let’s look at the types of interview questions you can expect throughout this process.

Below are sample interview questions you may encounter for a Data Analyst role at The Fund for Public Health in New York, Inc. These questions are designed to assess your technical expertise in data cleaning, analysis, pipeline design, and your ability to communicate findings to technical and non-technical stakeholders. Focus on demonstrating practical problem-solving, clear communication, and an understanding of public health data challenges.

3.1 Data Cleaning & Quality

Data quality is fundamental in public health analytics, where decisions depend on accurate and reliable information. Expect questions that probe your experience handling messy, incomplete, or inconsistent data, and your strategies for ensuring data integrity.

3.1.1 Describing a real-world data cleaning and organization project
Explain how you identified issues, prioritized fixes, and documented your process. Emphasize reproducibility and communication with stakeholders.

3.1.2 How would you approach improving the quality of airline data?
Discuss methods for profiling, cleaning, and validating large datasets, and how you’d prioritize fixes based on impact.

3.1.3 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Describe steps for transforming unstructured or inconsistent data into a usable format, and how you’d document your process for future audits.

3.1.4 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Outline your approach to data integration, identifying and resolving inconsistencies, and ensuring the final dataset is analysis-ready.

3.1.5 Write a query to find all dates where the hospital released more patients than the day prior
Demonstrate your ability to manipulate and analyze time-series healthcare data, using window functions or self-joins to compare daily metrics.

3.2 Data Analysis & Public Health Metrics

This category tests your ability to extract actionable insights from healthcare and community data. You may be asked to design metrics, analyze trends, and interpret results in a public health context.

3.2.1 Create and write queries for health metrics for stack overflow
Show how you’d define, calculate, and interpret key health metrics, and communicate them to stakeholders.

3.2.2 Write a SQL query to compute the median household income for each city
Demonstrate your understanding of aggregate functions and handling non-standard aggregations like medians.

3.2.3 Find a bound for how many people drink coffee AND tea based on a survey
Explain how you’d use set theory and survey data to estimate overlapping populations.

3.2.4 What strategies could we try to implement to increase the outreach connection rate through analyzing this dataset?
Discuss how you’d use data-driven insights to inform and optimize outreach strategies, focusing on measurable impact.

3.2.5 How would you design user segments for a SaaS trial nurture campaign and decide how many to create?
Describe your approach to segmentation, including data-driven criteria and how to evaluate the effectiveness of segments.

3.3 Data Communication & Visualization

Public health data analysts must clearly communicate findings to diverse audiences. Expect questions about tailoring insights, data storytelling, and making complex results actionable for non-technical stakeholders.

3.3.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe your approach to structuring presentations, using visuals, and adapting your message for different audiences.

3.3.2 Making data-driven insights actionable for those without technical expertise
Discuss strategies for simplifying technical results and focusing on business or public health impact.

3.3.3 Demystifying data for non-technical users through visualization and clear communication
Explain how you use visual tools and analogies to make data understandable and actionable.

3.3.4 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Describe visualization techniques for skewed or sparse data, and how you’d highlight key findings.

3.3.5 User Experience Percentage
Explain how you’d calculate and present user experience metrics, ensuring clarity for decision-makers.

3.4 Data Pipelines & Automation

Efficient data pipelines and automation are crucial for timely, reliable reporting in public health. These questions assess your ability to design, optimize, and maintain robust data workflows.

3.4.1 Design a data pipeline for hourly user analytics.
Outline your approach to data ingestion, transformation, and aggregation for real-time analytics.

3.4.2 Modifying a billion rows
Discuss scalable strategies for updating large datasets, focusing on performance and data integrity.

3.4.3 Describing a data project and its challenges
Share how you overcame technical or organizational obstacles in a data project, emphasizing problem-solving and adaptability.

3.4.4 Creating a machine learning model for evaluating a patient's health
Explain your process for building predictive models, including data preparation, feature selection, and communicating results to clinicians.

3.4.5 How would you approach acquiring 1,000 riders for a new ride-sharing service in a small city?
Describe how you’d use data to inform and measure the success of outreach or acquisition strategies.

3.5 Behavioral Questions

Behavioral questions help interviewers assess how you handle challenges, collaborate with teams, and communicate insights. Use specific examples from your experience, focusing on your impact and the reasoning behind your actions.

3.5.1 Tell me about a time you used data to make a decision.
3.5.2 Describe a challenging data project and how you handled it.
3.5.3 How do you handle unclear requirements or ambiguity?
3.5.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
3.5.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
3.5.6 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
3.5.7 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
3.5.8 How have you balanced speed versus rigor when leadership needed a “directional” answer by tomorrow?
3.5.9 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
3.5.10 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?

4.1 Company-specific tips:

Become deeply familiar with the mission and recent initiatives of The Fund for Public Health in New York, Inc. Review their annual reports, press releases, and recent collaborations with city agencies. Make sure you understand the organization’s approach to addressing health disparities and promoting innovative public health solutions. This background will help you tailor your interview answers to the organization’s values and priorities.

Stay up-to-date on public health challenges facing New York City. Research current issues like chronic disease prevention, infectious disease surveillance, health equity programs, and community outreach campaigns. Be ready to discuss how data analytics can support these initiatives and drive measurable improvements in community health.

Understand the unique data landscape of public health in New York City. Familiarize yourself with the types of datasets the organization may use, such as epidemiological surveillance data, program evaluation metrics, and health service utilization records. Consider the complexities of working with government and community data, including privacy concerns and data-sharing limitations.

4.2 Role-specific tips:

4.2.1 Practice cleaning and integrating messy, multi-source public health datasets.
Develop strategies for handling incomplete, inconsistent, or unstructured data from surveys, hospital records, and city databases. Practice documenting your cleaning process, resolving discrepancies, and preparing data for analysis so you can demonstrate your rigor and attention to detail in the interview.

4.2.2 Strengthen your proficiency in SQL and SAS for healthcare analytics.
Prepare to write queries that involve time-series analysis, aggregation of patient and program data, and calculation of epidemiological metrics like risk ratios and odds ratios. Be ready to explain your logic and the steps you take to ensure accuracy and reproducibility.

4.2.3 Review key public health metrics and epidemiological concepts.
Brush up on calculating and interpreting metrics such as prevalence, incidence, outreach connection rates, and cohort retention. Practice using these metrics to generate actionable insights and recommendations for program managers and policymakers.

4.2.4 Develop examples of communicating complex findings to non-technical audiences.
Prepare stories where you translated statistical results or technical data into clear, actionable recommendations for stakeholders with varying levels of expertise. Highlight your use of visualizations, analogies, and tailored messaging to make data accessible and impactful.

4.2.5 Prepare to discuss your experience designing and optimizing data pipelines.
Be ready to describe how you’ve built or improved workflows for collecting, cleaning, and reporting public health data. Focus on automation, scalability, and ensuring timely delivery of insights for program evaluation and decision-making.

4.2.6 Reflect on behavioral scenarios involving collaboration, ambiguity, and stakeholder management.
Think through examples where you balanced competing priorities, negotiated scope creep, or overcame communication challenges in cross-functional teams. Practice articulating your approach to problem-solving and maintaining project momentum, especially in a fast-paced, mission-driven environment.

4.2.7 Practice presenting analytical trade-offs and decision-making under uncertainty.
Prepare to discuss situations where you made judgment calls based on incomplete data, balanced speed versus rigor, or navigated ambiguous requirements. Emphasize your ability to communicate risks and justify your analytical choices to both technical and non-technical audiences.

5.1 How hard is the Fund for Public Health in New York, Inc. Data Analyst interview?
The interview is moderately challenging, especially for candidates new to public health analytics. Expect a balanced mix of technical questions (SQL, SAS, epidemiological metrics), case studies, and behavioral scenarios. The most demanding aspects are data cleaning, integrating complex health datasets, and clearly communicating insights to both technical and non-technical stakeholders. Candidates with experience in public sector data or health analytics will find the process more familiar.

5.2 How many interview rounds does the Fund for Public Health in New York, Inc. have for Data Analyst?
Typically, there are 4–6 rounds: resume/application screening, recruiter phone screen, technical/case interview (which may include a take-home assignment), behavioral interview, a final onsite or panel interview, and reference/background checks before offer negotiation.

5.3 Does the Fund for Public Health in New York, Inc. ask for take-home assignments for Data Analyst?
Yes, most candidates are given a take-home assignment, often focused on cleaning, analyzing, and presenting public health data. The assignment tests your ability to work with real-world, messy datasets and communicate actionable insights, usually within a 48-hour window.

5.4 What skills are required for the Fund for Public Health in New York, Inc. Data Analyst?
Key skills include advanced SQL and SAS programming, data cleaning and wrangling, statistical analysis (risk ratios, odds ratios, prevalence), data visualization, and the ability to translate complex findings for diverse audiences. Familiarity with public health datasets, experience with multi-source data integration, and strong communication skills are essential.

5.5 How long does the Fund for Public Health in New York, Inc. Data Analyst hiring process take?
The process typically takes 3–6 weeks from initial application to offer, though timelines can vary due to internal scheduling and project needs. Candidates should anticipate possible delays between stages, especially after panel interviews.

5.6 What types of questions are asked in the Fund for Public Health in New York, Inc. Data Analyst interview?
Expect technical questions on SQL/SAS, public health metrics, and data cleaning. Case studies often focus on analyzing and interpreting health program data. Behavioral questions probe collaboration, adaptability, and stakeholder communication. You may also be asked to present findings or walk through past projects.

5.7 Does the Fund for Public Health in New York, Inc. give feedback after the Data Analyst interview?
Feedback is usually provided through recruiters, especially if you complete a take-home assignment or reach the final interview stage. While detailed technical feedback may be limited, you can expect general insights into your interview performance and areas for improvement.

5.8 What is the acceptance rate for Fund for Public Health in New York, Inc. Data Analyst applicants?
The role is competitive, with an estimated acceptance rate of 3–7% for qualified applicants. Candidates with strong public health analytics backgrounds and demonstrated communication skills stand out.

5.9 Does the Fund for Public Health in New York, Inc. hire remote Data Analyst positions?
Yes, remote positions are available for Data Analysts, though some roles may require occasional in-person meetings or collaboration at the New York City office, depending on project needs and team structure.

• Related guides:
  • The Fund for Public Health in New York, Inc. interview questions
  • Data Analyst interview guide
  • Top data analyst interview tips

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