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Guideline Interview Questions

guideline 401(k). helping people save for a better future. guideline is the only 401(k) provider that doesn’t charge participants any fees on investments, regardless of the value of their assets or retirement account balance. and for employers, guideline charges a low, flat rate fee per participant, in contrast to the asset-based fee model predominant in the industry. by offering a full-stack solution that automates plan administration and compliance, the guideline team is combatting what’s unfortunately become the retirement industry status quo -- legacy providers have put profit over the interests of employees, abandoning the goal of the program in the first place: to give employees the opportunity to save as much as possible for retirement.

guideline 401(k). helping people save for a better future. guideline is the only 401(k) provider that doesn’t charge participants any fees on investments, regardless of the value of their assets or retirement account balance. and for employers, guideline charges a low, flat rate fee per participant, in contrast to the asset-based fee model predominant in the industry. by offering a full-stack solution that automates plan administration and compliance, the guideline team is combatting what’s unfortunately become the retirement industry status quo -- legacy providers have put profit over the interests of employees, abandoning the goal of the program in the first place: to give employees the opportunity to save as much as possible for retirement.

Overview

A/B Testing

Algorithms

Analytics

Machine Learning

Probability

Product Metrics

Python

Statistics

Guideline Interview Guides

Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned 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?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

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

What is a key disadvantage of the F-test when compared to multiple t-tests?

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

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

In an A/B test, how can you check if assignment to the various buckets was truly random?

In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?

In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?

Random Bucketing

A/B Testing & Experimentation

<h3>Job Description</h3><div>Job Description<div>Description:About GuidelineAt Guideline.ai, we're defining the future of advertising. By harnessing data transparency and advanced tools, we empower marketers to make smarter, faster, and more profitable decisions. We work with the world's top brands, agencies, and media owners to transform media buying and selling into a more intelligent, efficient experience.If you're ready to be part of a high-growth company at the cutting edge of data, AI, and media, we'd love to meet you. About the RoleWe’re looking for a Senior Data Product Analyst who thrives at the intersection of data, analytics, and product development. You’ll play a central role in shaping Guideline’s data products — helping to transform large, complex datasets into actionable insights, power new features, and ensure the reliability of our data across production and releases.This role combines analytical depth with product collaboration: working with engineers, product managers, and data strategists to evaluate datasets, automate QA workflows, and bring new capabilities to market.The ideal candidate combines strong analytical skills with a product-first mindset—someone who can turn data into strategy, shape product direction, and ensure the integrity of the data powering every release. Key Responsibilities<ul><li>Partner with Product and Engineering teams to research, evaluate, and develop new data-driven products and feature enhancements, conducting deep analyses on complex spend, pricing, and performance datasets to identify patterns, assess data viability, and inform design decisions from concept through production.</li><li>Contribute to go-to-market release execution by validating data in production environments, coordinating QA and feature testing, preparing product collateral, and delivering internal demos to align stakeholders on new functionality — all to drive successful adoption and launch readiness</li><li>Support release management by validating production data, investigating anomalies, providing analytical context for product and customer teams, and coordinating release communications to facilitate transparency and smooth deployment across teams.</li><li>Design and implement automated data validation and quality frameworks for digital products — defining thresholds, conditional formatting, and anomaly detection logic to proactively flag and resolve data issues, streamline QA processes, and ensure accuracy and reliability across releases.</li><li>Lead critical business-as-usual operations for monthly data releases, ensuring quality, reliability, and on-time delivery for customers by driving validation efforts, coordinating stakeholders, and resolving issues efficiently to support client retention and confidence.</li><li>Champion best practices in data governance, validation, and experimentation, establishing standards and documentation that enhance data quality, reliability, and analytical rigor across teams.</li><li>Play an active role in PI Planning, shaping backlog priorities, validating scope and acceptance criteria, and ensuring coordination across Product and Engineering teams to set realistic and value-driven delivery goals.</li><li>Leverage AI-powered analytical tools and LLM-based workflows to accelerate data validation, insight generation, and documentation.</li><li>Partner with engineering teams to integrate machine-learning–based anomaly detection and data quality checks into the product pipeline.</li><li>Identify product opportunities where AI or predictive modeling can enhance user experience, improve accuracy, or surface actionable insights.</li><li>Evaluate dataset readiness for AI/ML use cases, ensuring structures, definitions, and data governance support high-quality model performance</li></ul>Compensation: We consider a wide range of factors when determining compensation including relevant experience, education, and skill level. Benefits: Guideline offers full- time employees a comprehensive benefits package based on location. Some benefits may include, but are not limited to: - Health, dental, life, and disability insurance - RRSP with company match - Paid time off and parental leave - Teledoc Health services - Employee recognition and referral bonuses Equal Opportunity Employer: Guideline is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities, and veterans to apply.Requirements:Required Skills & Experience<ul><li>Extensive experience as a Data Analyst in a data-centric or product-driven environment, with the ability to translate analytical insights into product strategy and measurable business impact.</li><li>Strong analytical and problem-solving skills — able to identify trends, anomalies, and opportunities in large datasets.</li><li>Proficiency in SQL for querying, transforming, and validating complex datasets.</li><li>Hands-on experience with Power BI or equivalent BI tools (Tableau, Looker, Sisense, Qlik).</li><li>Demonstrated ability to communicate complex findings clearly, collaborate effectively with cross-functional teams, and influence decisions across Product, Engineering, and Operations.</li><li>Highly organized, detail-oriented, and proactive in managing multiple priorities and deadlines.</li></ul>Preferred Skills & Experience<ul><li>Background in media, advertising, or marketing data, with an understanding of spend, pricing, or campaign datasets.</li><li>Familiarity with MediaOcean (Prisma, Spectra), HudsonMX, or WideOrbit platforms.</li><li>Experience implementing automated QA systems, anomaly detection, or alerting frameworks for data quality.</li><li>Knowledge of JIRA, Confluence, and agile product development practices.</li><li>Strong data storytelling and business acumen, able to synthesize insights and present recommendations to senior stakeholders.</li><li>Familiarity with AI-assisted analytics tools (e.g., ChatGPT, Copilot, AI-based BI platforms).</li><li>Understanding of how data pipelines support AI models, including data labeling, data quality, and governance considerations.</li><li>Exposure to LLM workflows, embeddings, or vector-based search concepts (nice to have, not required).</li><li>Experience with Python-based data analysis or lightweight ML experimentation (scikit-learn preferred but not required).</li></ul></div></div>

<h3>Job Description</h3><div>Job Description<div>Description:About GuidelineAt Guideline.ai, we're defining the future of advertising. By harnessing data transparency and advanced tools, we empower marketers to make smarter, faster, and more profitable decisions. We work with the world's top brands, agencies, and media owners to transform media buying and selling into a more intelligent, efficient experience.If you're ready to be part of a high-growth company at the cutting edge of data, AI, and media, we'd love to meet you.About the RoleWe are looking for a Senior Data Analyst who is highly practical, detail-oriented, and strong in Python- and SQL-based data manipulation. This role is hands-on and focused on cleaning, standardizing, and enriching large, messy advertising spend datasets—especially digital data sourced from MediaOcean’s Prisma platform, where key information often lives inside inconsistent free-form text fields.In addition to data transformation and quality work, you’ll apply statistical and machine learning techniques to forecast spend trends, fill data gaps, and classify unstructured text data. You’ll also be expected to stay aware of emerging AI-assisted and generative AI technologies, understanding how they can improve analysis and automation even if you’re not directly building such models. What You’ll DoData Cleaning, Parsing & Standardization (Core Function)<ul><li>Build Python (Pandas/Regex) workflows to extract structure from free-form Prisma text fields containing placements, publishers, formats, devices, tactics, etc.</li><li>Develop rule-based and pattern-based logic to recognize abbreviation variations, naming inconsistencies, and agency-specific formatting habits.</li><li>Create normalization scripts that standardize publisher names, platform types, channels, and other key taxonomies.</li><li>Maintain “classification dictionaries” and pattern libraries that improve accuracy over time.</li></ul>Data Quality & Operational Support<ul><li>Profile incoming agency data to identify common quality issues and propose concrete remediation.</li><li>Design QA checks to detect missing fields, malformed entries, inconsistent dimensional labels, and mismatched spend totals.</li><li>Partner closely with Data Engineering to embed your logic into scheduled pipelines.</li><li>Own recurring operational workflows that support data ingestion, aggregation, and quality monitoring.</li></ul>Dimensional Enrichment, Modeling & Data Derivation<ul><li>Build and maintain predictive models to support media spend forecasting, pricing analysis, and text classification for data tagging and enrichment.</li><li>Apply statistical modeling or regression techniques to impute missing values or infer incomplete spend or performance metrics.</li><li>Derive new fields—such as format groupings, platform classifications, creative types, and targeting attributes—using pattern recognition and model-based inference across text fields.</li><li>Produce structured dimensions from unstructured data to improve downstream reporting.</li><li>Document the logic and assumptions behind model-driven derivations to ensure transparency and reproducibility.</li></ul>Analysis & Internal Stakeholder Support<ul><li>Investigate anomalies, unexpected trends, and data gaps by tracing issues back to source fields.</li><li>Build dashboards that help internal teams observe data quality, volume, deviations, and model-driven insights.</li><li>Provide ad-hoc deep dives into digital spend patterns, forecast accuracy, and classification performance for product, ops, and leadership.</li></ul>Why This Role MattersYour work directly determines the quality, usability, and credibility of the datasets our company provides to clients and uses internally. This is a core operational and analytical role, focused on making messy agency data usable at scale—while adding new analytical capabilities through modeling, forecasting, and intelligent automation. Compensation We consider a wide range of factors when determining compensation including relevant experience, education, and skill level. Benefits Guideline offers full- time employees a comprehensive benefits package based on location. Some benefits may include, but are not limited to:<ul><li>Medical, Dental, Vision, Health Savings Account, Flexible Spending Account</li><li>STD, Life, LTD and AD&D</li><li>401(k) with a company match program</li><li>Unlimited Paid Time Off (PTO)</li><li>Paid Parental Leave</li><li>Commuter Benefits</li><li>Employee Recognition Program</li><li>Referral Bonus Program</li></ul>Equal Opportunity Employer Guideline is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities, and veterans to apply.Requirements:What You Bring<ul><li>5+ years of experience as a Data Analyst or Data Operations Analyst.</li><li>Expert-level Python (Pandas, Regex) and SQL.</li><li>Experience extracting meaning from unstructured or text-heavy data.</li><li>Working knowledge of forecasting, regression, and basic statistical modeling (e.g., ARIMA, Prophet, linear regression, or similar methods).</li><li>Familiarity with text classification / NLP workflows (e.g., scikit-learn, spaCy, Hugging Face).</li><li>Awareness of AI-assisted development tools and basic understanding of generative AI / LLM concepts and their applications in data enrichment and automation.</li><li>Strong understanding of data hygiene, classification logic, and dimensional modeling.</li><li>Comfortable working in data warehouses (Snowflake, Redshift, etc.).</li><li>Ability to create repeatable, documented transformation logic.</li></ul></div></div>

<h3>Job Description</h3><div>Job Description<div>Description:About GuidelineAt Guideline.ai, we're defining the future of advertising. By harnessing data transparency and advanced tools, we empower marketers to make smarter, faster, and more profitable decisions. We work with the world's top brands, agencies, and media owners to transform media buying and selling into a more intelligent, efficient experience.If you're ready to be part of a high-growth company at the cutting edge of data, AI, and media, we'd love to meet you. About the RoleWe’re looking for a Senior Data Product Analyst who thrives at the intersection of data, analytics, and product development. You’ll play a central role in shaping Guideline’s data products — helping to transform large, complex datasets into actionable insights, power new features, and ensure the reliability of our data across production and releases. This role combines analytical depth with product collaboration: working with engineers, product managers, and data strategists to evaluate datasets, automate QA workflows, and bring new capabilities to market. The ideal candidate combines strong analytical skills with a product-first mindset—someone who can turn data into strategy, shape product direction, and ensure the integrity of the data powering every release. Key Responsibilities<ul><li>Partner with Product and Engineering teams to research, evaluate, and develop new data-driven products and feature enhancements, conducting deep analyses on complex spend, pricing, and performance datasets to identify patterns, assess data viability, and inform design decisions from concept through production. </li><li>Contribute to go-to-market release execution by validating data in production environments, coordinating QA and feature testing, preparing product collateral, and delivering internal demos to align stakeholders on new functionality — all to drive successful adoption and launch readiness </li><li>Support release management by validating production data, investigating anomalies, providing analytical context for product and customer teams, and coordinating release communications to facilitate transparency and smooth deployment across teams. </li><li>Design and implement automated data validation and quality frameworks for digital products — defining thresholds, conditional formatting, and anomaly detection logic to proactively flag and resolve data issues, streamline QA processes, and ensure accuracy and reliability across releases. </li><li>Lead critical business-as-usual operations for monthly data releases, ensuring quality, reliability, and on-time delivery for customers by driving validation efforts, coordinating stakeholders, and resolving issues efficiently to support client retention and confidence. </li><li>Champion best practices in data governance, validation, and experimentation, establishing standards and documentation that enhance data quality, reliability, and analytical rigor across teams. </li><li>Play an active role in PI Planning, shaping backlog priorities, validating scope and acceptance criteria, and ensuring coordination across Product and Engineering teams to set realistic and value-driven delivery goals. </li><li>Leverage AI-powered analytical tools and LLM-based workflows to accelerate data validation, insight generation, and documentation.</li><li>Partner with engineering teams to integrate machine-learning–based anomaly detection and data quality checks into the product pipeline.</li><li>Identify product opportunities where AI or predictive modeling can enhance user experience, improve accuracy, or surface actionable insights.</li><li>Evaluate dataset readiness for AI/ML use cases, ensuring structures, definitions, and data governance support high-quality model performance</li></ul>Compensation: We consider a wide range of factors when determining compensation including relevant experience, education, and skill level. Benefits: Guideline offers full- time employees a comprehensive benefits package based on location. Some benefits may include, but are not limited to:<ul><li>Medical, Dental, Vision, Health Savings Account, Flexible Spending Account</li><li>STD, Life, LTD and AD&D</li><li>401(k) with a company match program</li><li>Unlimited Paid Time Off (PTO)</li><li>Paid Parental Leave</li><li>Commuter Benefits</li><li>Employee Recognition Program</li><li>Referral Bonus Program</li></ul>Equal Opportunity Employer: Guideline is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities, and veterans to apply.Requirements:Required Skills & Experience<ul><li>Extensive experience as a Data Analyst in a data-centric or product-driven environment, with the ability to translate analytical insights into product strategy and measurable business impact. </li><li>Strong analytical and problem-solving skills — able to identify trends, anomalies, and opportunities in large datasets. </li><li>Proficiency in SQL for querying, transforming, and validating complex datasets. </li><li>Hands-on experience with Power BI or equivalent BI tools (Tableau, Looker, Sisense, Qlik). </li><li>Demonstrated ability to communicate complex findings clearly, collaborate effectively with cross-functional teams, and influence decisions across Product, Engineering, and Operations. </li><li>Highly organized, detail-oriented, and proactive in managing multiple priorities and deadlines. </li></ul>Preferred Skills & Experience<ul><li>Background in media, advertising, or marketing data, with an understanding of spend, pricing, or campaign datasets. </li><li>Familiarity with MediaOcean (Prisma, Spectra), HudsonMX, or WideOrbit platforms. </li><li>Experience implementing automated QA systems, anomaly detection, or alerting frameworks for data quality. </li><li>Knowledge of JIRA, Confluence, and agile product development practices. </li><li>Strong data storytelling and business acumen, able to synthesize insights and present recommendations to senior stakeholders. </li><li>Familiarity with AI-assisted analytics tools (e.g., ChatGPT, Copilot, AI-based BI platforms).</li><li>Understanding of how data pipelines support AI models, including data labeling, data quality, and governance considerations.</li><li>Exposure to LLM workflows, embeddings, or vector-based search concepts (nice to have, not required).</li><li>Experience with Python-based data analysis or lightweight ML experimentation (scikit-learn preferred but not required).</li></ul></div></div>

Question	Topic	Difficulty	Ask Chance
Boarding Times Bias	Statistics	Medium	Very High
Hundreds of Hypotheses	Statistics	Medium	Very High
Random Bucketing	A/B Testing & Experimentation	Medium	Very High

Guideline Interview Questions

Overview

Guideline Interview Guides

Guideline Interview Questions

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