SCITLS Lead Data Scientist

As a SCITLS Lead Data Scientist at Nexxen, you'll be responsible for leading a team of data scientists and engineers, mentoring them to foster a collaborative and innovative culture. You will oversee the development of scalable machine learning solutions that optimize real-time bidding and deliver business impact. Collaboration is key, as you'll work with cross-functional teams and communicate complex technical insights to both technical and non-technical stakeholders.

To be considered for the SCITLS Lead Data Scientist role at Nexxen, candidates should have a BS or MS in Computer Science, Engineering, or a related technical field, along with at least 7 years of experience in data science, machine learning, and statistical modeling. Additionally, a successful candidate should have 3+ years of experience managing data science teams and possess strong programming skills in Python, SQL, Node.js, Spark, and other relevant tools.

A SCITLS Lead Data Scientist at Nexxen should be proficient in several programming languages, particularly Python, SQL, and Node.js. Additionally, experience with Spark is essential for processing large data sets effectively, and familiarity with big data tools such as Vertica or Hadoop is a plus.

At Nexxen, the SCITLS Lead Data Scientist will benefit from a hybrid work schedule, allowing for flexibility between in-office and remote work. The team is typically in the office three days a week, providing opportunities for collaboration and teamwork, while enabling remote work for the remaining days.

The SCITLS Lead Data Scientist position at Nexxen offers a competitive salary range between $200,000 to $240,000 annually, depending on experience and qualifications. This includes the potential for bonuses or incentives, reflecting Nexxen's commitment to fair pay and transparency.

When discussing your experience leading data science teams, focus on specific examples where you not only managed projects but also mentored your team members, encouraging collaboration and innovation. Highlight any successful projects you've supervised, noting how you shaped team dynamics and fostered a culture of continuous learning.

In responding to this question, choose a specific data problem that showcases your analytical skills and problem-solving abilities. Describe the context, the approach you took, the tools you used, and the outcome. Emphasize how your actions led to actionable insights and positive business impacts.

Be prepared to discuss various machine learning models, including decision trees, neural networks, and regression models. Describe situations where you've implemented these models, the results achieved, and how you optimized their performance. This showcases your technical expertise and practical experience.

Discuss specific strategies you used to foster collaboration among teams. Highlight instances where you coordinated projects with product and engineering teams, emphasizing communication tactics and tools that helped bridge any gaps. Your answer should reflect your understanding of the importance of teamwork in achieving business objectives.

When answering this question, provide examples of situations where you navigated conflicting demands by actively listening to stakeholders. Stress the importance of transparency, aligning project goals with business objectives, and finding common ground, which serves to foster trust and collaboration.

Explain how you continuously educate yourself on the latest trends in data science. Mention attending conferences, enrolling in online courses, or participating in relevant online communities. This display of passion for your field showcases your commitment to staying ahead in a rapidly evolving industry.

In your answer, emphasize the role of data governance in ensuring data quality, compliance, and ethical use. Discuss how you have implemented governance frameworks in prior roles, focusing on the importance of maintaining trust and credibility with stakeholders and the entire organization.

When discussing statistical techniques, mention specific methods such as hypothesis testing, regression analysis, or A/B testing. Provide context on how you've leveraged these techniques to derive insights from data and the measurable outcomes that resulted from your analyses.

Detail your process for validating and tuning models, including cross-validation, grid search, or hyperparameter tuning. Provide examples of how your approach improved model performance based on real-world business results, reinforcing your technical competency and results-driven mindset.

Be honest about your preferred tools and technologies, whether it's Python, R, SQL, Spark, or others. Discuss specific projects where you utilized these tools and why they were critical to your success. Emphasizing your familiarity with current technologies demonstrates your readiness to integrate into Nexxen’s team.