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Data Analyst

Summary

The Operations Data Analyst will provide direct support to operational groups by converting data into high-value assets, such as insights and predictive models, that contribute to measurable improvements in business processes and performance. This role is responsible for identifying suitable data for the current business problems, collecting, joining, aggregating, validating, and cleansing data for analysis. In addition, they are responsible for building predictive models to improve long-term and short-term operational performance of the airline.


Visa Sponsorship Available

No


Minimum Requirements

Combination of Education and Experience will be considered. Must be authorized to work in the US as defined by the Immigration Act of 1986. Must pass a Criminal Background Check.

Education:  Bachelor’s Degree. A Bachelor’s degree in one of the following fields: Computer Science, Statistics, Mathematics, Engineering, Bioinformatics, Economics, Physics, Operations Research, or related fields.

Years of Experience:  Minimum three (3) years of experience in a technical and/or analytical environment.


•Technical experience working with data to include sourcing, extracting, validating, exploring, and transforming.

•Competency with Python and SQL.

•Initiative, curiosity, and problem-solving skills through personal development projects and ongoing education.

•Results driven, accepting responsibility for individual performance and contributions to team projects.

•Ability to foster healthy internal and cross-departmental professional relationships.

•Comfortable with a fast pace dynamic environment.

•Strong computer skills including but not limited to MS Office products.

•Ability and willingness to travel as required to conduct site visits.


Preferred Requirements

•Strong knowledge in one of these fields: statistics, data mining, operations research, econometrics, and/or information retrieval.

•Knowledge/experience in airline operations or analytics.

•Strong written and verbal communication skills, proven presentation skills to all levels of audience.

•Strong intellect and analytical aptitude, along with ability to be self-driven.

•Demonstrated proficiency in Python, R, MATLAB, SQL or other programing languages.

•Some AWS experience including SageMaker, Lambda functions and Athena.

•Some knowledge of different types of predictive machine learning algorithms.


Job Duties

•Translate advanced analytics problems into technical approaches that yield actionable recommendations, in diverse domains such as: predictive maintenance, delay prediction/recovery, crew behavior prediction, spare optimization.

•Conduct site visits, collaborate with subject matter experts, and oversee performance projects from initiation-to-completion.

•Communicate results and educate stakeholders and senior management through insightful visualizations, reports, and presentations.

•Coordinate and facilitate conversations with cross-functional groups in a collaborative environment.

•Work with operational leaders to understand key drivers and enhance modeling and forecast accuracy, identify areas of improvement, create a plan to address improvement areas, execute and provide reporting/analysis.

•Retrieve, prepare, and process a rich variety of data sources from structured and unstructured cloud and non-cloud sources during the development/exploratory process and work with Data Engineering to refine and deploy across environments.

•Perform exploratory data analysis to uncover trends and relationships and organize into actionable insights/products that can improve the operation.

•Act as an analytical mentor to others in the organization.

•Manage time effectively to meet deadlines across multiple projects.

•Leverage supervised machine learning algorithms like regression or classification to create models.

•Model Allegiant’s customer service standards in personal actions and when providing direction.

•Other duties as assigned.


Physical Requirements

The Physical Demands and Work Environment described here are a representative of those that must be met by a Team Member to successfully perform the essential functions of the role. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of the role.


Office - While performing the duties of this job, the Team Member is regularly required to stand, sit, talk, hear, see, reach, stoop, kneel, and use hands and fingers to operate a computer, key board, printer, and phone. May be required to lift, push, pull, or carry up to 20 lbs. May be required to work various shifts/days in a 24-hour situation. Regular attendance is a requirement of the role. Exposure to moderate noise (i.e. business office with computers, phones, printers, and foot traffic), temperature and light fluctuations. Ability to work in a confined area as well as the ability to sit at a computer terminal for an extended period of time. Some travel may be a requirement of the role.


Essential Services Provider

Allegiant as a national air carrier is deemed an essential service provider during declared national and state emergencies. Team Members will be required to report to their assigned trip or work location during national and state emergencies unless prohibited by local, state or federal order.


EEO Statement

We welcome all individuals from varied backgrounds and experiences to apply. Our company values the unique perspectives and talents that each person brings to our team.


Equal Opportunity Employer: Disability/Veteran

For more information, see https://allegiantair.jobs





$80,000 - $100,000 a year
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Average salary estimate

$90000 / YEARLY (est.)
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$80000K
$100000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Data Analyst, Allegiant

Are you ready to make a meaningful impact as a Data Analyst with Allegiant Air in beautiful Las Vegas, NV? In this dynamic role, you’ll be transforming raw data into high-value insights and predictive models that boost our airline's operational performance. Imagine diving deep into data analysis, identifying solutions for current business challenges, and using your technical prowess to create predictive models that shape the future of airline operations. You’ll be collaborating with talented teams, performing exploratory data analysis, and translating complex analytics into actionable recommendations. Your expertise in Python and SQL will be crucial in sourcing and validating data, while your strong communication skills will ensure you effectively present your findings to stakeholders. You’ll thrive in our fast-paced environment, where curiosity and problem-solving are key. With a Bachelor’s degree in a relevant field and at least three years of hands-on experience, you are well-equipped to tackle diverse analytical tasks. Plus, if you’re familiar with AWS and various machine learning algorithms, that’s a bonus! So, if you’re looking for a role where your contributions have a direct impact and you can foster cross-departmental relationships, this is the perfect opportunity for you to shine at Allegiant Air.

Frequently Asked Questions (FAQs) for Data Analyst Role at Allegiant
What are the main responsibilities of a Data Analyst at Allegiant Air?

As a Data Analyst at Allegiant Air, your primary responsibilities include transforming data into valuable insights, conducting exploratory data analysis, and building predictive models to enhance operational performance. You'll also collaborate with various teams to understand key drivers, communicate insights through visualizations, and oversee performance projects from initiation to completion.

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What education is required for the Data Analyst position at Allegiant Air?

To become a Data Analyst at Allegiant Air, you should hold a Bachelor’s degree in a relevant field such as Computer Science, Statistics, Mathematics, Engineering, or related areas. This educational background provides the necessary foundation for the technical and analytical tasks involved in the role.

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What technical skills should a Data Analyst possess at Allegiant Air?

A successful Data Analyst at Allegiant Air should be proficient in Python and SQL for data analysis, have experience with data sourcing and validation, and be familiar with AWS services. Additionally, knowledge of machine learning algorithms and strong analytical abilities are essential for creating predictive models and driving business improvements.

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How important is experience in airline operations for a Data Analyst role at Allegiant Air?

While prior experience in airline operations is preferred, it is not mandatory. What’s most important is your ability to leverage your analytical skills to address operational challenges. A strong interest in the airline industry and a passion for data-driven decision-making will serve you well in this role.

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Can a Data Analyst at Allegiant Air work remotely or must they be onsite?

The Data Analyst position at Allegiant Air primarily requires onsite work in Las Vegas, NV, due to the collaborative nature of the role and the need for regular communication with various operational teams. However, some flexibility may be available depending on project needs and company policies.

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Common Interview Questions for Data Analyst
How do you approach data validation in your analysis?

When asked about data validation, focus on your systematic approach. Discuss methods like cross-verifying data against authoritative sources, checking for consistency, and using statistical techniques to identify outliers and anomalies. Be sure to highlight your attention to detail and how these practices enhance data integrity in your analysis.

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Describe a successful project where you used predictive modeling.

For this question, share a specific project where predictive modeling created significant value. Explain your approach—how you selected the variables, the model you used, and the outcomes. Emphasize teamwork, the challenge faced, and how your insights led to actionable business improvements.

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What tools do you prefer for data visualization and why?

Discuss your familiarity with tools such as Tableau, Power BI, or Python libraries like Matplotlib and Seaborn. Explain why you choose certain tools based on the audience, complexity of the data, and the story you wish to convey. Mention how effective visualizations can aid in better decision-making.

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How do you communicate complex data findings to non-technical stakeholders?

Emphasize your ability to simplify complex concepts through clear visuals and concise language. Describe how you would tailor your presentations to the audience’s level of understanding, using real-world analogies and focusing on actionable insights that can help in decision-making.

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What experience do you have with statistical analysis techniques?

Share your experience with various statistical techniques such as regression analysis, hypothesis testing, and A/B testing. Provide examples of how you applied these techniques in real-world scenarios to draw insights that contributed to project successes.

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Can you explain the importance of exploratory data analysis (EDA)?

When explaining EDA, emphasize its role in uncovering patterns, trends, and anomalies in datasets before formal modeling begins. Highlight how EDA can guide data cleaning and inform decision-making, ensuring that follow-on analyses are grounded in a thorough understanding of the data.

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Describe your experience with cross-functional collaboration.

Talk about instances where you worked with different departments to achieve a common goal. Highlight your communication skills and how you build relationships, making it easier to share data-driven insights and recommendations. Demonstrating adaptability and team spirit is key here.

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What motivates you as a data analyst?

Express your passion for problem-solving and deriving insights from data. Mention how the impact of your work on operational efficiency and decision-making within an organization excites you, and how continuous learning in this rapidly evolving field keeps you motivated.

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How familiar are you with machine learning algorithms?

Discuss any exposure you have to machine learning algorithms and how you've used them in past projects. Explain the algorithms you are familiar with, such as regression, classification, or clustering techniques, and provide examples of their applications in predictive modeling.

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What do you consider the biggest challenge in data analysis?

Acknowledge common challenges such as data quality issues or misalignment between data metrics and business objectives. Share strategies you use to address these challenges, such as thorough validation processes or collaborating closely with stakeholders to ensure that data analyses remain focused on key business needs.

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Full-time, on-site
DATE POSTED
March 5, 2025

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