May Mobility Data Scientist interview questions & guide 2026

The questions you will encounter during the interview process are designed to evaluate your practical data manipulation skills, statistical rigor, and ability to translate complex data into business decisions. These questions are representative of past interviews and are structured to test your approach to real-world, ambiguous problems rather than rote memorization.

Data Manipulation & Coding

• How would you clean and preprocess a highly noisy dataset containing GPS coordinates and timestamps from an autonomous vehicle?
• Write a Python script to merge two large datasets: one containing vehicle trip logs and the other containing passenger ride requests, optimizing for memory efficiency.
• How do you handle missing or corrupted sensor data in a time-series dataset without biasing your downstream models?

To succeed in the May Mobility interview process, you must demonstrate a unique blend of technical execution, statistical intuition, and structured problem-solving. The hiring team looks for candidates who can operate independently and bring clarity to unstructured data environments.

Role-Related Knowledge – You must possess strong foundations in statistical modeling, data manipulation, and metrics design. This includes proficiency in Python or R, SQL, and hands-on experience with data analysis libraries (such as pandas and NumPy). You should be comfortable setting up your own local development environment to analyze complex datasets.

Problem-Solving & Ambiguity – Because the company's products are in an active R&D stage, you will frequently face problems with no predefined solution. Interviewers will evaluate how you structure vague questions, define key performance indicators (KPIs), and make reasonable assumptions when data is incomplete or messy.

Communication & Collaboration – As a Data Scientist, you will collaborate closely with software engineers, product managers, and operations teams. You must be able to translate complex mathematical and statistical concepts into clear, actionable recommendations for non-technical stakeholders.

The interview process at May Mobility is structured to assess your hands-on technical capabilities early in the cycle. It balances technical execution with behavioral alignment, ensuring that candidates possess both the coding skills and the collaborative mindset required to thrive in a fast-paced R&D environment.

You will begin with a brief recruiter screen to discuss your background and interest in the company. Following a successful screen, the process moves quickly to a rigorous technical take-home assessment. This assessment is a critical gatekeeper in the hiring pipeline and must be completed before you proceed to interviews with the hiring manager or the broader data science team.

The timeline above outlines the standard progression from initial contact to the final decision. Candidates should expect a heavy emphasis on practical execution during the take-home stage, followed by deep-dive discussions regarding their analytical choices in subsequent rounds. Use this timeline to pace your preparation, ensuring your local coding environment is fully configured before initiating the technical assessment.

Technical Take-Home Assessment

The technical take-home assessment is a defining element of the May Mobility hiring process. It is designed to simulate a real-world data science task that you would encounter on the job, testing your ability to work with raw data and deliver structured insights under a deadline.

Be ready to go over:

• Local Environment Setup – You will need a fully functional local development environment (typically Python or R) on your personal computer, as the assessment requires running analysis libraries and manipulating datasets locally.
• Data Cleaning & Exploratory Data Analysis (EDA) – Expect to receive a raw, semi-structured dataset. You must demonstrate your ability to handle missing values, outliers, and time-series alignments.
• Insight Generation & Visualization – Strong performance involves not just writing code, but compiling your findings into a clean, readable format (such as a Jupyter Notebook or a brief report) that clearly explains your methodology and conclusions.

Example scenarios:

• "Analyze a dataset of vehicle telemetry to identify patterns in system disengagements."
• "Perform exploratory data analysis on passenger trip data to recommend optimal fleet charging schedules."

Metrics Design & Statistical Inference

As a Senior Data Scientist - Metrics, a core component of your evaluation will center on your ability to design robust, reliable metrics that accurately reflect system performance and user experience.

Be ready to go over:

• KPI Formulation – Defining metrics that balance technical accuracy with business utility (e.g., defining safety metrics that do not unnecessarily penalize cautious driving behavior).
• A/B Testing & Experimentation – Designing experiments in non-traditional environments where standard web-based A/B testing assumptions (like independent and identically distributed observations) may not hold.
• Advanced statistical concepts – Bootstrap sampling, survival analysis for vehicle component lifespans, and spatial-temporal modeling of fleet movements.

Example scenarios:

• "Design a metric to evaluate the efficiency of a vehicle's lane-change decision-making process."
• "How would you structure an experiment to prove that a software update reduced average passenger trip times?"

As a Data Scientist at May Mobility, your day-to-day responsibilities will revolve around extracting value from the massive amounts of data generated by the autonomous fleet. You will be responsible for defining, building, and monitoring the metrics that determine the success of the autonomous driving system and the operational efficiency of the business.

You will collaborate closely with Software Engineers to ensure that data pipelines are optimized for downstream analysis, and work with Product Managers to define the success criteria for new features. Because the product is in an active R&D stage, a significant portion of your time will be spent investigating anomalies in fleet behavior, designing custom statistical tests, and presenting data-driven recommendations to leadership.

Additionally, you will play a key role in democratizing data across the organization. This involves building self-service visualization tools, documenting data schemas, and mentoring junior team members on best practices for data analysis and statistical inference.

To be competitive for the Data Scientist position, particularly at the senior level, you must demonstrate a strong track record of delivering analytical solutions to complex, real-world problems.

• Technical Skills – High proficiency in Python or R, strong SQL skills, and experience with data visualization libraries (such as Plotly, Seaborn, or Tableau). Experience with big data technologies (like Spark or AWS Athena) is highly beneficial.
• Experience Level – Typically 5+ years of experience in a data science or analytical role, with a proven background in metrics design, statistical modeling, or product analytics.
• Soft Skills – Excellent communication skills, the ability to manage stakeholder expectations, and a proactive approach to resolving ambiguity.

Must-Have vs. Nice-to-Have

• Must-have skills – Strong statistical foundation (hypothesis testing, regression, experimental design), proficiency in data manipulation, and the ability to work independently on a local development environment.
• Nice-to-have skills – Experience working with geospatial data, time-series data from IoT devices/sensors, or prior experience in the autonomous vehicle or transportation logistics industries.

Q: How long do I have to complete the technical take-home assessment? You are typically given two to three days to complete the assessment once it is sent to you. The assessment is designed to take approximately 3 to 6 hours of focused work, depending on your familiarity with the dataset style.

Q: Can I skip the take-home assessment if I have a strong GitHub portfolio? No. The technical take-home assessment is a mandatory step in the May Mobility interview process for all Data Scientist candidates. It ensures a standardized, objective evaluation of core coding and analytical skills across all applicants.

Q: Is the work environment fully remote, hybrid, or onsite? While May Mobility has its headquarters in Ann Arbor, MI, work arrangements depend on the specific team and role requirements. Many data science positions offer hybrid or remote flexibility, but you should clarify expectations with your recruiter during the initial call.

Q: What is the primary tech stack used by the data science team? The team primarily utilizes Python for data analysis, modeling, and scripting, alongside SQL for data extraction. Cloud infrastructure is heavily integrated, utilizing modern data warehousing and pipeline tools to manage autonomous vehicle telemetry.

• Set up your environment early: Ensure you have a clean, working local environment with standard data science packages (pandas, numpy, scikit-learn, matplotlib/seaborn) installed before you request or receive the take-home test.

• Focus on the "Why" in your analysis: When presenting your take-home results or discussing past projects, do not just explain what models you used. Focus heavily on why you chose those specific metrics, what assumptions you made, and how those decisions impact the business.

• Be prepared for R&D ambiguity: Remember that May Mobility is dealing with novel engineering challenges. Be ready to discuss how you approach situations where there is no historical baseline or "ground truth" data available.

The Data Scientist role at May Mobility offers an exceptional opportunity to tackle some of the most complex, high-impact challenges in the autonomous vehicle industry. By designing the metrics that evaluate vehicle safety, passenger comfort, and operational efficiency, you will directly influence the evolution of autonomous transportation.

To succeed, focus your preparation on solidifying your statistical fundamentals, refining your metrics-design framework, and ensuring your local development setup is ready for hands-on data analysis. Approaching the take-home assessment with structure, clear documentation, and business-focused insights will set you apart from other candidates.

For additional practice questions, company deep dives, and community insights from candidates who have navigated similar pipelines, explore the resources available on Dataford.

The salary range shown above represents the base compensation for the Senior Data Scientist - Metrics position in Ann Arbor, MI. Actual offers are determined by a combination of factors, including the candidate's depth of experience, technical expertise, and overall performance throughout the interview loop.