Director of Data Science

The Director of Data Science at Liminal Insights leads and manages the data science team, drives innovation in inspection analytics, and oversees the development of data science products. They also conduct hands-on machine learning projects, ensuring that insights are communicated effectively to both internal and external stakeholders.

Candidates should have a minimum of 10 years in data science with at least 3 years in a leadership role. A strong background in areas such as physics-based modeling and signal processing is essential, as well as proficiency in machine learning frameworks like TensorFlow and PyTorch.

Liminal Insights prides itself on creating a world-class, inclusive culture built on trust, empathy, and curiosity. The company encourages personal growth, values diverse perspectives, and strives to create a positive impact in the clean energy sector.

The Director of Data Science plays a key customer-facing role by delivering technical findings and analytics results related to battery manufacturing processes. Engaging with customers to identify their challenges and proposing data-driven solutions is a critical aspect of the role.

While the main qualifications involve extensive experience in data science and leadership, nice-to-have attributes include familiarity with cloud platforms like AWS, MLOps practices, experience in manufacturing analytics, and a degree in fields such as Physics or materials science.

When discussing your machine learning experience, highlight specific projects where you applied machine learning algorithms, the challenges faced, and how your solutions positively impacted the outcomes. Be sure to emphasize your proficiency in relevant frameworks.

Share your leadership philosophy, focusing on mentorship and fostering a collaborative environment. Discuss how you encourage skill development and address challenges that arise within your team.

Illustrate your approach by detailing specific physics-based modeling techniques you've used. Discuss how you've integrated these methods into your data science workflows to address complex manufacturing challenges.

Use a specific example to demonstrate your communication skills. Emphasize how you tailored your presentation to the audience's needs, focusing on key insights that drove decision-making.

Discuss your familiarity with various signal processing techniques and how you've applied them to improve data quality in complex data sets. Provide examples of successful implementations in your previous work.

Explain your approach for maintaining close collaboration with cross-functional teams. Illustrate how you align data science objectives with business needs to create impactful solutions.

Share strategies such as attending workshops, participating in conferences, and engaging in ongoing education. Highlight specific areas of interest and how they had a direct impact on your work.

Discuss methodologies you've utilized for deploying models effectively, including considerations for scalability, monitoring, and performance optimization once in production.

Describe your decision-making process for project prioritization, taking into account business impact, resource availability, and alignment with your strategic goals.

Choose a specific instance where you tackled a complex data problem. Discuss the approach you took, the tools used, and the outcome, focusing on how it improved operations or analytics accuracy.