Lead Data Scientist - GenAI

As a Lead Data Scientist - GenAI at Tiger Analytics, you will be responsible for collaborating with business partners to create advanced analytical models, developing generative AI products, and leading cross-functional teams in innovative projects. You will ensure these models are efficient and accurate while driving strategy and business value through your analytical insights.

The Lead Data Scientist - GenAI position at Tiger Analytics requires 8+ years of overall experience and at least 5 years focused specifically on GenAI. Candidates should have advanced skills in Python, experience with machine learning algorithms such as LSTM and transformers, and a solid understanding of generative AI frameworks like Bedrock and HuggingFace.

Essential technical skills for the Lead Data Scientist - GenAI role at Tiger Analytics include proficiency in Python, familiarity with supervised and unsupervised learning methods, and hands-on experience developing generative algorithms like GAN and VAE. Additionally, experience with ethical AI considerations and model deployment is crucial.

At Tiger Analytics, a Lead Data Scientist - GenAI can expect significant career growth opportunities in a fast-paced entrepreneurial environment. You will not only have a high degree of responsibility but also engage in advanced projects that enhance your skills and position within the analytics industry.

Tiger Analytics fosters a collaborative culture among its Data Scientists by encouraging teamwork and knowledge sharing. Lead Data Scientists are expected to coach and learn from their peers, providing a nurturing environment that promotes continuous improvement and innovation.

When discussing your experience with generative AI models, highlight specific projects where you developed or fine-tuned models. Describe the outcomes and how you contributed to improving their performance, emphasizing your understanding of algorithms like GANs and VAEs.

Talk about your experience merging disparate internal and external data sources. Provide examples of how your business acumen aided in feature engineering and how this contributed to model performance and business insights.

When answering this question, mention specific metrics you use, such as accuracy, recall, F1 score, or business KPIs, and elaborate on the methods you implement for reassessment and improvement of models over time.

Describe the techniques you implement for model fine-tuning, such as adjusting hyperparameters, retraining with new data, or utilizing transfer learning. Provide insights into the decision-making process behind these methodologies.

Detail a specific project where you successfully worked with cross-functional teams. Highlight the roles involved, your contributions, and how collaboration led to successful project outcomes.

Mention your proficiency in Python as a primary language and any other relevant tools or languages you've worked with, such as R or SQL. Discuss your hands-on experience with libraries and frameworks that are relevant to generative AI.

Explain how you engage with the data science community through conferences, workshops, online courses, and publications. Mention any specific communities or sources you find particularly valuable for industry insights.

Discuss the ethical implications you assess when working with AI, such as bias detection and mitigation, transparency, and user privacy, emphasizing how these considerations shape your project decisions.

Outline the steps you take in transitioning models from development to deployment, such as testing in staging environments, monitoring performance, and ensuring robustness under various conditions. Make sure to mention the importance of collaboration with engineering teams in this process.

Share your approach to receiving and integrating feedback from stakeholders. Discuss how you prioritize constructive criticism and use it to refine your models or project strategies, emphasizing the importance of open communication.