Senior Data Scientist - job 1 of 2

As a Senior Data Scientist at Palta, your key responsibilities will involve developing and deploying machine learning models, utilizing modern data science methods to tackle business challenges, and collaborating with development, analytics, and business teams. Your focus will be on ensuring high-quality outputs and driving the continuous improvement of our technologies.

To qualify for the Senior Data Scientist position at Simple, candidates should have at least four years of experience in data science with a strong proficiency in machine learning algorithms. Expertise in specific domains, such as recommendation systems or time-series analysis, along with programming experience in Python and familiarity with libraries like TensorFlow or PyTorch is also essential.

The ideal Senior Data Scientist at Palta should be proficient in Python and have solid experience with core machine learning libraries like TensorFlow, PyTorch, and Scikit-learn. Additionally, knowledge of SQL, Git, Docker, and experience working with large language models (LLMs) is highly advantageous.

At Simple, we offer a flexible work environment with the option to work remotely or in a hybrid format from our Limassol office. Whether you’re based in Cyprus or elsewhere, we ensure our team has the right equipment and support to thrive in their roles.

As a Senior Data Scientist at Palta, you can expect a competitive salary package complemented by stock options, premium subscriptions to our health and wellness apps, and ample vacation time, including 21 days of annual leave plus bank holidays. We prioritize the well-being and engagement of our employees!

In answering this question, detail specific algorithms you've worked with, perhaps focusing on those you've implemented in real-world projects. Highlight your understanding of their applications, any challenges you've faced, and the results achieved.

Start by discussing your systematic approach to model development, including data collection, preprocessing, model selection, and validation. Emphasize the importance of monitoring deployed models and adjusting pipelines for performance improvement.

Mention your experience working cross-functionally, focusing on effective communication, project management tools, and methods you utilize to ensure all stakeholders have their requirements met for optimal project outcomes.

Choose a specific example that showcases your problem-solving skills. Explain the initial problem, your analytical approach, the resolution process, and the successful outcome. Highlight any learnings from the experience.

Discuss a concrete project where you leveraged Python effectively, emphasizing your proficiency with libraries like Pandas for data manipulation or TensorFlow for machine learning, showcasing your technical skills and project impact.

Explain your methods for validating model performance, including techniques like cross-validation, assessing metrics such as accuracy or precision, and how you gather feedback from users or monitor real-world use to enhance models.

Discuss your previous work with LLMs, detailing your experience in fine-tuning models, writing prompts, and applications you've built using these models. Highlight how your work aligns with Simple's focus on engaging health solutions.

Talk about innovative trends such as automated machine learning or advancements in AI ethics that you believe are shaping the future of data science. Show your passion for continuous learning and adaptation in this rapidly evolving field.

Convey your understanding of teamwork and collaboration. Discuss the importance of sharing knowledge, mentoring less experienced team members, and fostering a positive workplace environment that encourages creative energy and innovation.

Identify key performance indicators relevant to data science, such as model accuracy, user engagement metrics, or business outcomes. Emphasize your commitment to aligning project goals with the broader objectives of Simple.