Data Scientist (Generative AI)

As a Data Scientist (Generative AI) at Globality, your core responsibilities will involve implementing and optimizing features using pre-trained Generative AI models and developing solutions like retrieval-augmented generation (RAG) and prompt engineering. You will also integrate Large Language Models into applications, enhance their outputs, and collaborate with cross-functional teams to drive AI-driven innovations that meet business needs.

To be considered for the Data Scientist (Generative AI) role at Globality, candidates should ideally have 3+ years of experience in data science or a related field, along with a Master’s or Ph.D. in AI/ML, Computer Science, Statistics, or a pertinent STEM discipline. Proficiency in AI/ML techniques, experience with integrating LLMs, familiarity with frameworks like TensorFlow or PyTorch, and strong skills in Python and SQL are also required.

At Globality, the work environment for a Data Scientist (Generative AI) is collaborative and innovative. You’ll work in a hybrid setting, requiring at least three days a week in our Palo Alto HQ, which allows for face-to-face teamwork with various departments. Our culture emphasizes trust, collaboration, and inclusion, creating a supportive space for creativity and problem-solving.

Globality believes in fostering continuous learning and professional growth. Data Scientists (Generative AI) are encouraged to stay updated on the latest advancements in Generative AI and can recommend best practices and tools. The company provides resources that facilitate learning, collaboration, and the opportunity to work on groundbreaking projects that contribute to your career development.

The anticipated annual salary for the Data Scientist (Generative AI) role at Globality ranges from $140,000 to $200,000. This range varies based on individual factors such as experience, location, and performance. It’s important to note that this is just one part of Globality's comprehensive compensation package, which considers diverse factors to ensure fair compensation.

In responding to this question, provide specific examples of projects where you utilized Generative AI models, the techniques you employed, and the outcomes you achieved. Highlight your familiarity with tools like LLMs and how your contributions improved business processes or user experience.

Discuss a specific instance where you successfully integrated an AI/ML solution, detailing the challenges faced, how you approached the problem, and the techniques used. Emphasize your role in collaborating with other teams to ensure seamless integration.

Share the optimization techniques you commonly apply, such as hyperparameter tuning, feature engineering, and evaluation metrics. Explain your approach to iterating on features and making data-driven decisions to enhance model performance.

Discuss the resources and methods you utilize to keep updated, such as attending conferences, joining online forums, or following research papers and industry news. Mention any specific trends in AI that have influenced your work.

Ensure to explain this concept clearly, emphasizing how retrieval-augmented generation combines generative capabilities with information retrieval to improve accuracy and relevance in AI outputs. Provide examples of where you’ve applied this technique.

When answering, describe your thought process in constructing effective prompts, considering factors like context, detail, and intended outcomes. Use examples to showcase how prompt engineering impacts the success of AI models.

Talk about your experience with data handling, including strategies for cleaning, preprocessing, and analyzing large datasets. Mention any specific tools or frameworks that you find particularly effective.

Choose a specific challenge, explain the steps you took to overcome it, and what you learned from the experience. This demonstrates your problem-solving skills and ability to adapt in a fast-paced environment.

Highlight your communication strategies for working with stakeholders from marketing, product management, and engineering. Discuss your methods for explaining complex AI concepts in an understandable way and ensuring that their needs are met.

Share your personal motivations for pursuing a career in AI, whether it's the potential for innovation, the impact on industries, or problem-solving capabilities. Make it relatable to show your passion for the field.