Interview Guides

HERE Technologies Data Scientist Interview Guide 2026

HERE Technologies

Data Scientist

What is a Data Scientist at HERE Technologies?

As a Data Scientist at HERE Technologies, your role is pivotal in unlocking the value of data to enhance location-based services and solutions. You will work at the intersection of technology and geography, utilizing advanced analytical techniques to derive insights that inform product development and strategic decisions. This position is crucial as it directly influences the effectiveness and innovation of HERE Technologies' offerings, impacting users and businesses globally.

In this role, you will engage with large-scale datasets, applying machine learning algorithms and statistical methods to solve complex problems. You'll collaborate with cross-functional teams, including engineering, product management, and operations, to develop and implement data-driven solutions. The challenges you face will range from optimizing routing algorithms to enhancing geospatial analytics, making this role both exciting and strategically significant in a rapidly evolving field.

Common Interview Questions

The interview questions for the Data Scientist position at HERE Technologies will vary by team and project needs but are drawn from common themes observed in past interviews. Expect a mix of technical, behavioral, and problem-solving questions. This guide illustrates key patterns rather than providing a rote list of questions.

Technical / Domain Questions

This category tests your knowledge of data science principles and their application in real-world scenarios.

Describe a machine learning project you've worked on and the impact it had.
What are the differences between supervised and unsupervised learning?
How do you evaluate the performance of a model?
Can you explain the concept of overfitting and how to prevent it?
What techniques would you use for feature selection in a dataset?

Coding / Algorithms

You will likely face questions that assess your programming skills and understanding of algorithms.

Write a function to calculate the mean and standard deviation of a list of numbers.
How would you implement a decision tree algorithm from scratch?
Explain how you would optimize a piece of code for better performance.
Describe a time when you had to debug a complex piece of code.
What data structures are most suitable for implementing a priority queue?

Behavioral / Leadership

These questions evaluate your soft skills, including teamwork and communication.

Describe a challenging situation you faced in a team project and how you handled it.
How do you prioritize your tasks when working on multiple projects?
What motivates you to perform well in your role?
Can you give an example of how you've influenced a team decision?
How do you handle feedback and criticism?

Problem-Solving / Case Studies

Expect to encounter real-world scenarios that require analytical thinking.

Given a dataset, how would you approach the problem of predicting user behavior?
How would you design a system to recommend products to users?
Discuss a case where you had to make a decision with incomplete data.
If tasked with improving a routing algorithm, what factors would you consider?
How would you assess the effectiveness of a new feature in a mapping application?

Machine Learning / Data Engineering

Questions in this area will delve deeper into specific technologies and methodologies.

What is your experience with deep learning frameworks like TensorFlow or PyTorch?
Explain the concept of transfer learning and its benefits.
How do you ensure data quality and integrity in your projects?
Describe how you would deploy a machine learning model into production.
What tools do you use for data visualization and why?

See every interview question for this role

Practice questions from our question bank

Curated questions for HERE Technologies from real interviews. Click any question to practice and review the answer.

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Easy

Pipelines

Handle Missing Values in ETL

Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.

ETL

Data Wrangling

Quality

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Easy

SQL & Data Manipulation

Handling Missing Values in SQL

Explain how to detect and handle NULL values in SQL using filtering, COALESCE, CASE, and business-aware imputation.

Aggregations

Case When

Data Wrangling

Easy

Model Evaluation

Explain Cross-Validation to Executives

Explain why cross-validation gives a more trustworthy view of model performance than a single strong test split.

Cross-Validation

Accuracy

Calibration

Easy

Model Evaluation

Compare Precision-Recall Tradeoffs

Compare two classifiers with high-precision vs high-recall behavior and recommend the better model under business cost and review-capacity constraints.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate F1 Score Significance in Model Performance

Analyze the significance of the F1 score in a binary classification model for customer churn prediction, and propose improvements.

Accuracy

F1 Score

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Pipelines

Build Data Quality Controls Pipeline

Design a batch ETL pipeline that validates CRM, billing, and product data before loading curated Snowflake tables.

Data Modeling

ETL

Quality

Easy

SQL & Data Manipulation

Classify Orders with CASE WHEN

Explain how CASE WHEN adds conditional logic to SQL queries for labeling, transforming, and aggregating data.

Aggregations

Case When

Data Wrangling

Easy

Pipelines

Ensure Data Quality in ETL

Design a Snowflake ETL pipeline that enforces schema, deduplication, reconciliation, and auditable data quality checks for finance data.

Data Modeling

ETL

Quality

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Medium

Model Evaluation

Evaluate Model Metrics for Customer Churn Prediction

Analyze why a customer churn prediction model has low recall despite high precision and propose actionable improvements.

Easy

Metrics

Measure Checkout Funnel Conversion Rate

Define overall and step-level funnel conversion for an e-commerce checkout flow and explain how to diagnose where drop-off occurs.

KPIs

Conversion Rate

Funnel Analysis

Easy

SQL & Data Manipulation

Handling Missing Demographic Data

Explain how to assess, quantify, and handle missing demographic fields in SQL without distorting downstream analysis.

Subqueries

Case When

Data Wrangling

Easy

SQL & Data Manipulation

Detect and Handle Outliers in SQL

Explain common SQL-friendly ways to detect outliers and how to handle them without distorting downstream analysis.

Aggregations

Group By

Data Wrangling

Easy

Machine Learning

Compare Bagging and Boosting for Claims Risk

Explain and compare bagging vs boosting by training tree-based ensembles to predict high-cost insurance claims.

Ensemble Methods

Bias-Variance Tradeoff

Decision Trees

Easy

Statistics & Probability

A/B Test for Ranking Model Value

Use a two-proportion z-test to determine whether a new ranking model significantly improves recommendation CTR in an A/B test.

A/B Testing

Statistical Significance

Experimentation

Easy

Model Evaluation

Choose Metrics for Business Impact

Decide whether precision, recall, F1-score, or RMSE best fits fraud detection and demand forecasting given asymmetric business costs.

Accuracy

Precision

Recall

+2 more

Medium

Product Sense

Investigate Engagement Drop with Stable ARR

Analyze the root cause of a 10% engagement drop while ARR remains flat to inform product strategy.

User Needs

Engagement Metrics

Churn

+2 more

Sign up to see all questions

Create a free account to access every interview question for this role.

Getting Ready for Your Interviews

Preparation for your interviews at HERE Technologies is crucial. You should focus on demonstrating both your technical expertise and your ability to collaborate effectively with diverse teams. Here are key evaluation criteria that interviewers will assess:

Role-related Knowledge – You will need a strong foundation in data science concepts, including machine learning, statistics, and data engineering. Interviewers will look for your ability to discuss these topics clearly and apply them to practical scenarios.

Problem-Solving Ability – Candidates are expected to showcase their analytical thinking and structured approach to solving complex problems. Be prepared to walk through your thought process and justify your decisions.

Leadership – Even as a data scientist, demonstrating leadership qualities is essential. This includes how you communicate ideas, influence team dynamics, and navigate challenges in collaborative environments.

Culture Fit / Values – HERE Technologies values team collaboration and innovation. Your ability to align with these values and demonstrate adaptability in a fast-paced environment will be key.

Interview Process Overview

The interview process for a Data Scientist at HERE Technologies is designed to be thorough yet supportive, reflecting the company's commitment to finding the right fit. Typically, candidates can expect an initial phone screening, followed by multiple rounds of interviews that may include technical assessments, behavioral interviews, and case studies. The pace is moderate, allowing you to engage meaningfully with your interviewers.

Throughout the process, HERE Technologies emphasizes a collaborative approach, aiming to assess not just your technical skills, but also your potential contributions to team dynamics and overall company culture. Interviews are generally structured to create a comfortable environment where candidates can showcase their expertise and passion for data science.

This visual timeline illustrates the key stages in the interview process, from initial screening to onsite interviews. Utilize this to plan your preparation effectively and manage your energy throughout the interview stages, ensuring you're ready for each component of the assessment.

Deep Dive into Evaluation Areas

Understanding how you will be evaluated is critical to your success. Here are key evaluation areas relevant to the Data Scientist role at HERE Technologies:

Technical Expertise

This area is fundamental as it pertains to your knowledge of data science methods and your proficiency in programming.

Interviewers will assess your familiarity with data manipulation and analysis tools.
Expect detailed questions on machine learning algorithms, statistical methods, and data visualization techniques.
Strong candidates will articulate complex concepts clearly and demonstrate practical knowledge through examples.

Example Questions:

Explain how you would approach a time-series forecasting problem.
What machine learning libraries are you most comfortable with, and why?

Problem-Solving Skills

Your ability to tackle real-world problems with innovative solutions will be evaluated.

You may be presented with case studies or hypothetical scenarios to solve.
Interviewers will look for structured thinking and creativity in your approach to challenges.

Example Questions:

How would you design an experiment to test a new feature in a mapping application?

Collaboration and Communication

Your interpersonal skills are as important as your technical abilities.

Demonstrating how you communicate complex ideas to non-technical stakeholders is crucial.
Strong candidates will provide examples of successful teamwork and conflict resolution.

Example Questions:

Describe a time when you had to explain a technical concept to someone without a technical background.

Cultural Fit

Aligning with the company’s values and culture is vital for long-term success.

Interviewers will assess your adaptability and openness to feedback.
Expect questions that explore your motivations and work style.

Example Questions:

How do you incorporate feedback into your work?

Key Responsibilities

As a Data Scientist at HERE Technologies, your responsibilities will revolve around leveraging data to drive insights and inform product development. You will:

Analyze vast datasets to uncover trends and patterns that can enhance product features and user experiences.
Collaborate with cross-functional teams, including engineering and product management, to develop data-driven solutions.
Contribute to the design and implementation of algorithms that improve location-based services.
Present findings and recommendations to stakeholders, translating complex data insights into actionable strategies.
Continuously explore new data sources and tools to improve the accuracy and efficiency of analyses.

Your role will require a blend of technical skills, creativity, and collaborative spirit, ensuring you contribute meaningfully to the innovative projects at HERE Technologies.

Role Requirements & Qualifications

To be a competitive candidate for the Data Scientist position at HERE Technologies, you should possess the following qualifications:

Must-have skills:
- Proficiency in programming languages such as Python or R.
- Experience with machine learning frameworks (e.g., TensorFlow, Scikit-learn).
- Strong knowledge of statistical analysis and data visualization tools (e.g., Tableau, Matplotlib).
- Familiarity with SQL and data manipulation techniques.
Nice-to-have skills:
- Experience with cloud platforms (AWS, Azure, Google Cloud).
- Understanding of geospatial data and mapping technologies.
- Knowledge of deep learning and natural language processing.
Experience level:
- Typically, candidates should have 2-5 years of relevant experience in data science or a related field.
- A strong academic background in mathematics, statistics, computer science, or engineering is preferred.
Soft skills:
- Excellent communication and teamwork abilities.
- Strong analytical and critical thinking skills.
- Ability to manage multiple projects and deadlines in a fast-paced environment.

Frequently Asked Questions

Q: What is the typical interview difficulty for this position?
The interview process for a Data Scientist at HERE Technologies tends to be average to difficult, with a strong emphasis on technical and problem-solving skills. Candidates should prepare thoroughly to demonstrate their expertise.

Q: How much preparation time is typical?
Candidates generally benefit from several weeks of preparation, focusing on technical skills, coding practice, and understanding of machine learning concepts.

Q: What differentiates successful candidates?
Successful candidates often possess a blend of strong technical skills and the ability to communicate effectively with non-technical stakeholders. Demonstrating a collaborative mindset is also key.

Q: What is the culture like at HERE Technologies?
HERE Technologies values innovation, teamwork, and a commitment to excellence. The work environment encourages collaboration and open communication, making it important for candidates to align with these values.

Q: How long does the hiring process typically take?
The timeline from initial screening to offer can vary but usually spans several weeks. Candidates should be prepared for multiple interview stages.

Q: Are remote work options available?
HERE Technologies offers flexibility in work arrangements, with options for hybrid and remote work depending on team needs and location.

Other General Tips

Research the Company: Understanding HERE Technologies' mission and products will help you answer questions more effectively and demonstrate your alignment with the company’s values.
Practice Coding: Be prepared for live coding sessions or technical assessments. Use platforms like LeetCode or HackerRank to sharpen your skills.
Prepare Your Stories: Develop narratives around your past projects and experiences that highlight your problem-solving abilities and teamwork.
Ask Questions: Prepare thoughtful questions about the team and company culture to demonstrate your genuine interest in the role.

Tip

Be honest about your salary expectations during the interview, as compensation discussions can be sensitive. Prepare to defend your value based on market research.

Summary & Next Steps

In conclusion, the Data Scientist role at HERE Technologies is both challenging and rewarding, offering the opportunity to make a significant impact on location-based services. Successful candidates will focus on honing their technical skills, preparing for a variety of questions, and understanding the company culture.

As you prepare, remember to emphasize both your technical expertise and your collaborative spirit. Engaging with the interview process thoughtfully can greatly enhance your chances of success.

Explore additional interview insights and resources on Dataford, and approach your preparation with confidence, knowing that your skills and experiences can lead to a successful career at HERE Technologies.

The salary insights provided here will help you understand the compensation landscape for the Data Scientist role. Use this information to set realistic expectations and prepare for discussions during the interview process.

Sign up to read the full guide

Create a free account to unlock the complete interview guide with all sections.

HERE Technologies

Data Scientist

Build my plan

What is a Data Scientist at HERE Technologies?

Common Interview Questions

Technical / Domain Questions

This category tests your knowledge of data science principles and their application in real-world scenarios.

Describe a machine learning project you've worked on and the impact it had.
What are the differences between supervised and unsupervised learning?
How do you evaluate the performance of a model?
Can you explain the concept of overfitting and how to prevent it?
What techniques would you use for feature selection in a dataset?

Coding / Algorithms

You will likely face questions that assess your programming skills and understanding of algorithms.

Write a function to calculate the mean and standard deviation of a list of numbers.
How would you implement a decision tree algorithm from scratch?
Explain how you would optimize a piece of code for better performance.
Describe a time when you had to debug a complex piece of code.
What data structures are most suitable for implementing a priority queue?

Behavioral / Leadership

These questions evaluate your soft skills, including teamwork and communication.

Describe a challenging situation you faced in a team project and how you handled it.
How do you prioritize your tasks when working on multiple projects?
What motivates you to perform well in your role?
Can you give an example of how you've influenced a team decision?
How do you handle feedback and criticism?

Problem-Solving / Case Studies

Expect to encounter real-world scenarios that require analytical thinking.

Given a dataset, how would you approach the problem of predicting user behavior?
How would you design a system to recommend products to users?
Discuss a case where you had to make a decision with incomplete data.
If tasked with improving a routing algorithm, what factors would you consider?
How would you assess the effectiveness of a new feature in a mapping application?

Machine Learning / Data Engineering

Questions in this area will delve deeper into specific technologies and methodologies.

What is your experience with deep learning frameworks like TensorFlow or PyTorch?
Explain the concept of transfer learning and its benefits.
How do you ensure data quality and integrity in your projects?
Describe how you would deploy a machine learning model into production.
What tools do you use for data visualization and why?

See every interview question for this role

Practice questions from our question bank

Curated questions for HERE Technologies from real interviews. Click any question to practice and review the answer.

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Easy

Pipelines

Handle Missing Values in ETL

Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.

ETL

Data Wrangling

Quality

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Easy

SQL & Data Manipulation

Handling Missing Values in SQL

Explain how to detect and handle NULL values in SQL using filtering, COALESCE, CASE, and business-aware imputation.

Aggregations

Case When

Data Wrangling

Easy

Model Evaluation

Explain Cross-Validation to Executives

Explain why cross-validation gives a more trustworthy view of model performance than a single strong test split.

Cross-Validation

Accuracy

Calibration

Easy

Model Evaluation

Compare Precision-Recall Tradeoffs

Compare two classifiers with high-precision vs high-recall behavior and recommend the better model under business cost and review-capacity constraints.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate F1 Score Significance in Model Performance

Analyze the significance of the F1 score in a binary classification model for customer churn prediction, and propose improvements.

Accuracy

F1 Score

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Pipelines

Build Data Quality Controls Pipeline

Design a batch ETL pipeline that validates CRM, billing, and product data before loading curated Snowflake tables.

Data Modeling

ETL

Quality

Easy

SQL & Data Manipulation

Classify Orders with CASE WHEN

Explain how CASE WHEN adds conditional logic to SQL queries for labeling, transforming, and aggregating data.

Aggregations

Case When

Data Wrangling

Easy

Pipelines

Ensure Data Quality in ETL

Design a Snowflake ETL pipeline that enforces schema, deduplication, reconciliation, and auditable data quality checks for finance data.

Data Modeling

ETL

Quality

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Medium

Model Evaluation

Evaluate Model Metrics for Customer Churn Prediction

Analyze why a customer churn prediction model has low recall despite high precision and propose actionable improvements.

Easy

Metrics

Measure Checkout Funnel Conversion Rate

Define overall and step-level funnel conversion for an e-commerce checkout flow and explain how to diagnose where drop-off occurs.

KPIs

Conversion Rate

Funnel Analysis

Easy

SQL & Data Manipulation

Handling Missing Demographic Data

Explain how to assess, quantify, and handle missing demographic fields in SQL without distorting downstream analysis.

Subqueries

Case When

Data Wrangling

Easy

SQL & Data Manipulation

Detect and Handle Outliers in SQL

Explain common SQL-friendly ways to detect outliers and how to handle them without distorting downstream analysis.

Aggregations

Group By

Data Wrangling

Easy

Machine Learning

Compare Bagging and Boosting for Claims Risk

Explain and compare bagging vs boosting by training tree-based ensembles to predict high-cost insurance claims.

Ensemble Methods

Bias-Variance Tradeoff

Decision Trees

Easy

Statistics & Probability

A/B Test for Ranking Model Value

Use a two-proportion z-test to determine whether a new ranking model significantly improves recommendation CTR in an A/B test.

A/B Testing

Statistical Significance

Experimentation

Easy

Model Evaluation

Choose Metrics for Business Impact

Decide whether precision, recall, F1-score, or RMSE best fits fraud detection and demand forecasting given asymmetric business costs.

Accuracy

Precision

Recall

+2 more

Medium

Product Sense

Investigate Engagement Drop with Stable ARR

Analyze the root cause of a 10% engagement drop while ARR remains flat to inform product strategy.

User Needs

Engagement Metrics

Churn

+2 more

Sign up to see all questions

Create a free account to access every interview question for this role.

Getting Ready for Your Interviews

Interview Process Overview

Deep Dive into Evaluation Areas

Understanding how you will be evaluated is critical to your success. Here are key evaluation areas relevant to the Data Scientist role at HERE Technologies:

Technical Expertise

This area is fundamental as it pertains to your knowledge of data science methods and your proficiency in programming.

Interviewers will assess your familiarity with data manipulation and analysis tools.
Expect detailed questions on machine learning algorithms, statistical methods, and data visualization techniques.
Strong candidates will articulate complex concepts clearly and demonstrate practical knowledge through examples.

Example Questions:

Explain how you would approach a time-series forecasting problem.
What machine learning libraries are you most comfortable with, and why?

Problem-Solving Skills

Your ability to tackle real-world problems with innovative solutions will be evaluated.

You may be presented with case studies or hypothetical scenarios to solve.
Interviewers will look for structured thinking and creativity in your approach to challenges.

Example Questions:

How would you design an experiment to test a new feature in a mapping application?

Collaboration and Communication

Your interpersonal skills are as important as your technical abilities.

Demonstrating how you communicate complex ideas to non-technical stakeholders is crucial.
Strong candidates will provide examples of successful teamwork and conflict resolution.

Example Questions:

Describe a time when you had to explain a technical concept to someone without a technical background.

Cultural Fit

Aligning with the company’s values and culture is vital for long-term success.

Interviewers will assess your adaptability and openness to feedback.
Expect questions that explore your motivations and work style.

Example Questions:

How do you incorporate feedback into your work?

Key Responsibilities

As a Data Scientist at HERE Technologies, your responsibilities will revolve around leveraging data to drive insights and inform product development. You will:

Analyze vast datasets to uncover trends and patterns that can enhance product features and user experiences.
Collaborate with cross-functional teams, including engineering and product management, to develop data-driven solutions.
Contribute to the design and implementation of algorithms that improve location-based services.
Present findings and recommendations to stakeholders, translating complex data insights into actionable strategies.
Continuously explore new data sources and tools to improve the accuracy and efficiency of analyses.

Your role will require a blend of technical skills, creativity, and collaborative spirit, ensuring you contribute meaningfully to the innovative projects at HERE Technologies.

Role Requirements & Qualifications

To be a competitive candidate for the Data Scientist position at HERE Technologies, you should possess the following qualifications:

Must-have skills:
- Proficiency in programming languages such as Python or R.
- Experience with machine learning frameworks (e.g., TensorFlow, Scikit-learn).
- Strong knowledge of statistical analysis and data visualization tools (e.g., Tableau, Matplotlib).
- Familiarity with SQL and data manipulation techniques.
Nice-to-have skills:
- Experience with cloud platforms (AWS, Azure, Google Cloud).
- Understanding of geospatial data and mapping technologies.
- Knowledge of deep learning and natural language processing.
Experience level:
- Typically, candidates should have 2-5 years of relevant experience in data science or a related field.
- A strong academic background in mathematics, statistics, computer science, or engineering is preferred.
Soft skills:
- Excellent communication and teamwork abilities.
- Strong analytical and critical thinking skills.
- Ability to manage multiple projects and deadlines in a fast-paced environment.

Frequently Asked Questions

Q: Are remote work options available?
HERE Technologies offers flexibility in work arrangements, with options for hybrid and remote work depending on team needs and location.

Other General Tips

Research the Company: Understanding HERE Technologies' mission and products will help you answer questions more effectively and demonstrate your alignment with the company’s values.
Practice Coding: Be prepared for live coding sessions or technical assessments. Use platforms like LeetCode or HackerRank to sharpen your skills.
Prepare Your Stories: Develop narratives around your past projects and experiences that highlight your problem-solving abilities and teamwork.
Ask Questions: Prepare thoughtful questions about the team and company culture to demonstrate your genuine interest in the role.

Tip

Be honest about your salary expectations during the interview, as compensation discussions can be sensitive. Prepare to defend your value based on market research.

Summary & Next Steps

As you prepare, remember to emphasize both your technical expertise and your collaborative spirit. Engaging with the interview process thoughtfully can greatly enhance your chances of success.

Sign up to read the full guide

Create a free account to unlock the complete interview guide with all sections.