Data Scientist (Applied Machine Learning)

As a Data Scientist (Applied Machine Learning) at Nory, your main responsibilities will include designing, building, and deploying machine learning algorithms, monitoring and improving existing models, and collaborating with product and engineering teams to release new features. You will have the opportunity to engage deeply with both the research and engineering aspects of machine learning, helping to solve key problems in the hospitality industry.

To be considered for the Data Scientist (Applied Machine Learning) position at Nory, candidates should possess a strong foundation in machine learning theory, practical experience in deploying algorithms, and proficiency in writing clean Python code. Familiarity with cloud infrastructure and a scientific approach to problem-solving are also essential. Previous experience with ML projects is highly advantageous.

Yes, the Data Scientist (Applied Machine Learning) role at Nory offers flexible working arrangements. You can work remotely from anywhere within the GMT +1 time zone or choose a hybrid option from our offices in Dublin or Holborn. This flexibility aims to accommodate your preferences and support a healthy work-life balance.

At Nory, we foster a vibrant and inclusive culture where diverse perspectives are welcomed and valued. As a Data Scientist, you'll be part of a collaborative environment that encourages creativity and innovation. We prioritize personal development with access to learning resources, regular team offsites, and a strong emphasis on maintaining a healthy work-life integration.

Nory offers a comprehensive benefits package to its Data Scientists, including equity at our Series A valuation, 35 days of paid leave per year, private health insurance, enhanced parental leave, and a personal learning budget. Additionally, there is a home office workspace budget to support your work environment, ensuring you have all the tools needed to succeed.

In answering this question, provide specific examples of past projects where you successfully deployed machine learning algorithms into production. Focus on the challenges you faced and how you overcame them, the technologies used, and the impact of your work on the business or project goals.

Explain your structured approach to starting a new machine learning project, including steps such as defining the problem, collecting and pre-processing data, selecting models, conducting experiments, and evaluating results. Highlight the importance of testing hypotheses and iterating based on findings.

Discuss specific machine learning techniques you have experience with, such as supervised vs. unsupervised learning, neural networks, or ensemble methods. Share projects or scenarios where you applied these techniques and the results achieved.

Provide an overview of your strategies to manage overfitting and underfitting, such as using cross-validation, selecting appropriate model complexity, utilizing regularization techniques, and monitoring model performance using validation data. Give practical examples of how you've applied these strategies.

Detail your experience with cloud platforms, such as AWS, Google Cloud, or Azure. Discuss how you utilized these services for data storage, model training, deployment, and scaling processes. Highlight how this experience has improved your workflow and efficiency in managing machine learning projects.

Share the importance of selecting the right evaluation metrics for different types of machine learning tasks. Discuss metrics such as accuracy, precision, recall, F1 score, and AUC-ROC, explaining when to use each and how they relate to the project objectives.

Mention various ways you keep pace with the fast-evolving field of machine learning, such as reading research papers, attending conferences, participating in online courses, and engaging in community forums. Discuss how this continuous learning enhances your work at Nory.

Share a specific example of a challenging machine learning problem. Describe the context, your approach to solving it, the tools and techniques you used, and the outcomes. Emphasize your problem-solving skills and creativity in overcoming obstacles.

Highlight your ability to communicate effectively with cross-functional teams. Talk about how you gather requirements, share insights, and advocate for machine learning integrations. Provide examples of successful collaborations that led to impactful feature releases.

Discuss the steps you typically take for data preprocessing, such as data cleaning, normalization, feature selection, and transformation. Emphasize the importance of these steps in ensuring model accuracy and relevancy in real-world applications.