Engineering Manager, Data and ML

As the Engineering Manager, Data and ML at BenchSci, you will lead a talented engineering team, overseeing the design, development, and deployment of data and machine learning solutions. Your role includes mentoring team members, managing project lifecycles, and ensuring that the team’s efforts contribute effectively to the company's mission. You will also tackle challenging technical problems and collaborate cross-functionally to integrate solutions seamlessly.

To qualify for the Engineering Manager, Data and ML position at BenchSci, candidates should have at least four years of professional development experience, with a minimum of two years in an engineering management role. Strong proficiency in Python and experience with machine learning solutions, particularly with online and batch inference, are critical. A deep understanding of data engineering principles and cloud platforms will also be necessary to succeed in this role.

An Engineering Manager, Data and ML at BenchSci should have robust technical skills in programming, particularly with Python, and familiarity with designing machine learning solutions that address various needs. Experience with cloud environments like AWS, GCP, or Azure is also essential. Candidates should understand data engineering best practices, distributed systems, and have experience with event-driven data pipelines to ensure project success.

BenchSci is committed to the professional growth of its Engineering Manager, Data and ML, offering a robust learning and development budget to enhance skills continuously. Employees also benefit from an inclusive culture that encourages personal development, mentorship opportunities, and resources to support career advancement. The company’s values emphasize transparency, collaboration, and continuous learning, ensuring a supportive environment.

The work culture for the Engineering Manager, Data and ML at BenchSci is remote-first, inclusive, and dedicated to fostering a high-performance atmosphere. The company values diversity and encourages a collaborative environment where team members can thrive. BenchSci emphasizes employee well-being, transparency, and ongoing personal development, making it a rewarding place for Engineering Managers to excel and contribute to meaningful work.

When answering this question, highlight specific projects where you utilized Python for data analysis or machine learning tasks. Discuss libraries like TensorFlow or scikit-learn and how you've applied them to solve real-world problems. Mention any challenges faced and how you overcame them, showcasing your technical proficiency and problem-solving skills.

Interviewers are keen to hear about your leadership style. Discuss your approach to mentoring, setting clear expectations, and providing constructive feedback. Highlight how you balance hands-on involvement with delegation, creating an environment of trust and accountability to enhance team motivation and performance.

Effective communication is key in any management role. Share examples of how you establish regular check-ins, feedback loops, and collaborative tools to maintain clarity. Describe how you adapt your communication style to match different audiences, ensuring that technical concepts are accessible to non-technical stakeholders.

Strongly focus on describing your understanding of agile methodologies or whatever framework you prefer. Mention how you manage requirements, prioritize backlogs, and conduct retrospectives. Tailor your response to have examples of successful projects that went from conception to deployment, showcasing your organizational skills.

When asked this question, share a specific scenario where you faced a challenging problem in a project. Discuss your thought process in analyzing the issue, the innovative solutions you proposed, and the result of your actions. Use metrics to quantify your success whenever applicable.

Be specific about any data engineering frameworks you have worked with and describe how you built or managed data pipelines. Share your understanding of online inference, including the technology stack you’ve used, and give examples of projects where you implemented these technologies.

Interviewers value candidates who can manage uncertainty. Share techniques like stakeholder engagement, scope definition, and incremental delivery that help navigate ambiguity. Provide examples of how you adapt projects to changing conditions without compromising quality or deadlines.

Discuss your approach to performance reviews, setting clear KPIs, and encouraging continuous feedback. Explain how you identify skill gaps and provide training or mentorship that helps elevate team performance. Show that you value continuous improvement.

Detail your hands-on experience using Google Cloud Platform services, such as BigQuery, Dataflow, or Vertex AI, and how they were instrumental in projects. Highlight the benefits these technologies provided and discuss any challenges faced during implementation.

Interviewers want to see your commitment to professional growth. Discuss the resources you use to stay updated, such as articles, online courses, and conferences. Share how you apply new knowledge and technologies to improve your work or team performance.