What are the best ways to discuss a company's data science projects during an interview?

Here are some best ways to approach this discussion: Research the company: Before the interview, thoroughly research the company's data science projects, their goals, and the technologies they use. Highlight relevant experience: Discuss any previous data science projects or experiences you have had that are similar to the company's projects. Explain the methodologies, techniques, and tools you used, and emphasize the impact and results you achieved. Discuss problem-solving skills: Data science projects often involve solving complex problems. Communicate effectively: Data scientists need to effectively communicate their findings and insights to stakeholders.

In case, it is an open ended question to describe any data science project, choose a project based on the job role and company's domain + type of problem statements they are working on. Structure to be followed to explain a data science project: 1. Objective: Explain what exactly you were trying to achieve? Hypothesis of the problem statement. 2. Description of the project: features used, data size, methodologies used. Also explain how you acquired/collected the data, if data cleaning was required, if there were any methodologies or algorithm did not work for you. 3. Outcome: Explain the outcome (put the accuracy or other numbers of model performance). How it helped company in improving the performance or streamlining a process.

Remember, the interview is an opportunity to showcase not only your technical skills but also your problem-solving abilities, communication skills, and how well you can contribute to the company's data science endeavours. Here are some effective ways to discuss a company's data science projects during an interview: 1. Research the Company's Projects 2. Understand the Problem Statement 3. Highlight Relevant Experience 4. Discuss Methodologies and Techniques 5. Demonstrate Problem-Solving Skills 6. Quantify Results and Impact 7. Communicate Clearly and Concisely 8. Ask Intelligent Questions 9. Highlight Collaboration and Communication Skills 10. Be Honest About Limitations

It is important to research the company's industry, business model, and any publicly available information about their data science initiatives. Redirect your discussion to align with their context.

In Kundenpräsentationen hat sich für mich der folgende Aufbau bewährt: 1. Problemstellung: Welche Problemstellung hatte der Kunde, warum ist diese für Ihn wichtig und was wurde bereits zur Lösung unternommen? 2. Ziele: Was waren die Ziele und wie sollte der Erfolg gemessen werden (Metriken)? 3. Daten: Welche Ausgangsdaten / Beschränkungen standen zur Verfügung und wie sahen diese grob aus? 4. Lösungsansatz und Durchführung: Wie sollte das Problem gelöst werden? Welche Schritte waren notwendig? Wie viele Iterationen / Experimente mussten durchlaufen werden? 5. Ergebnisse: Wie sahen die Ergebnisse aus? Konnten Mehrwerte generiert werden? 6. Probleme und Hindernisse: Was hat während der Durchführung nicht geklappt?

During the interview, be prepared to share your own experience working on data science projects. Talk about the types of projects you've worked on in the past, the technologies and tools you've used, and any challenges you've faced. This will help the interviewer understand your skills and experience in this area.

Throughout the interview, look for opportunities to connect your skills and experience with the company's data science initiatives. For example, if they mentioned a specific technology or tool they use, mention that you're already familiar with it. If they talked about a particular challenge they're facing, offer some insights or suggestions based on your own experience. This will help them see how you could add value to their team.

At the end of the interview, ask for feedback on your performance and whether there are any areas where you could improve. This shows that you're open to learning and willing to improve your skills, which is a valuable trait in a data scientist. It also gives you an opportunity to address any concerns the interviewer may have had during the interview.

Do not forget to ask questions. Engage in a two-way conversation. Ask questions about the company's current data science challenges and opportunities, showcasing your genuine interest in their specific context.

Before the interview, take some time to research the company's data science initiatives and projects. Look for recent news, press releases, and case studies on their website or LinkedIn page. This will help you understand their data science strategy and the types of projects they're working on.

1. Make sure to ask about the roles and responsibilities of the data science project. 2. Know more about the future projects planned by the company