Intern - Data Science

As an Intern - Data Science at FWLab, you will engage in critical tasks such as using scientific methods to develop risk models, leveraging machine learning solutions, and presenting your findings to stakeholders, including senior management. Your role will involve analyzing large datasets to provide meaningful insights that support effective decision-making within the city.

To qualify for the Intern - Data Science position at FWLab, candidates must have a Bachelor's degree in Mathematics, Statistics, Physics, Econometrics, or a related quantitative field. Additionally, preferred qualifications include knowledge of machine learning techniques, experience in programming languages like Python, and a creative approach to data analysis.

While prior experience in data science is beneficial, candidates who possess relevant work experience may be considered even if they do not meet the formal educational requirements for the Intern - Data Science role at FWLab. It's essential to demonstrate a strong understanding of quantitative methods and familiarity with data analysis tools.

During your internship at FWLab, you will gain valuable skills such as advanced data analysis using statistical methods, machine learning techniques, and proficiency in programming languages like Python. You will also enhance your presentation and communication abilities by conveying complex ideas to various stakeholders effectively.

The work schedule for the Intern - Data Science at FWLab is Monday to Friday from 8 am to 5 pm, with some occasional evening and weekend work required. This flexibility helps accommodate project needs and offers you a chance to experience real-world data challenges.

When asked about a previous data analysis project, describe the project’s objectives, the data you used, the methods applied for analysis, and the insights gained. Focus on the impact your findings had and how you communicated them to stakeholders.

Discuss specific machine learning techniques such as linear regression, decision trees, or clustering algorithms. Provide examples of how you've applied these techniques in past projects or studies to demonstrate your understanding and practical application.

Explain your process for data cleaning, which may include handling missing values, removing outliers, and normalizing data. Emphasize your attention to detail and how you ensure data quality before analysis.

Share a specific example of a challenge such as data inconsistencies or unexpected results. Explain how you approached the situation analytically, what solutions you implemented, and the outcome of those actions.

Mention resources like online courses, webinars, or data science blogs you follow. It’s also good to highlight any communities or forums you participate in, showing your proactive approach to continuous learning.

Highlight your expertise in programming languages relevant to data science, such as Python or R, and provide specific examples of projects you’ve completed using these languages to showcase your skills.

Illustrate your communication strategy by providing a clear and simplified explanation of your findings, possibly through visuals or analogies, showcasing your ability to bridge the gap between technical and non-technical stakeholders.

Express your passion for contributing to a dynamic environment like FWLab and the meaningful work involved in supporting the Fort Worth community through data-driven decisions. Highlight how the role aligns with your career aspirations.

Discuss your familiarity with tools like Tableau, Power BI, or Python libraries such as Matplotlib or Seaborn. Provide examples of how you've utilized these tools to create impactful visualizations in previous projects.

Emphasize your communication skills and previous experiences where you've presented data findings to audiences, confidently stating how you prepare for these presentations to ensure clarity and engagement.