About the company
Nexthink is the leader in digital employee experience management software. The company provides IT leaders with unprecedented insight allowing them to see, diagnose and fix issues at scale impacting employees anywhere, with any application or network, before employees notice the issue. As the first solution to allow IT to progress from reactive problem solving to proactive optimization, Nexthink enables its more than 1,300 customers to provide better digital experiences to more than 18 million employees. Dual headquartered in Lausanne, Switzerland and Boston, Massachusetts, Nexthink has 9 offices worldwide.
Responsibilities
- Design & Ship Data Products: Architect, build, and ship reliable, well-documented data products and services (from pipelines to APIs) that internal teams and external customers can depend on.
- Data Lake Infrastructure: Build, scale, and maintain the data lake and its underlying pipelines (e.g., using Spark), ensuring data is reliably ingested, processed, and structured for both analytical and production use.
- Applied Data Science: Explore large-scale datasets to uncover patterns, quantify product impact, and inform the design of the systems and metrics your team builds.
- AI/ML Leverage: Leverage AI to enhance the development process and the quality of the software and data products your team delivers.
- Collaboration & Leadership: Work closely with data engineers, ML engineers, and product teams to align data capabilities with business needs across the stack.
- Mentorship & Coaching: Guide and support junior engineers and data scientists, fostering a culture of learning and knowledge sharing.
Requirements
- BSc/Master's degree (or PhD) in Computer Science, Data Science, Machine Learning, Statistics, or a related field.
- 5+ years of hands-on experience as a software engineer working on data-intensive systems, ideally with direct data science or applied ML exposure.
- Strong software engineering fundamentals — clean architecture, testing, CI/CD — applied to data and ML workflows.
- Hands-on experience with distributed data processing frameworks (e.g., Spark) and building/maintaining data lake infrastructure at scale.
- Proven experience with cloud-based data platforms (AWS preferred).
- Strong proficiency in Python and standard data tooling (e.g., pandas, NumPy, scikit-learn, SQL).
- Solid data intuition: ability to inspect raw logs, design metrics, and quickly identify quality regressions.
- Working knowledge of experimental design, statistical inference, and A/B testing methodology.
- Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.
- Strong communication skills in English, capable of explaining complex technical concepts to both technical and non-technical stakeholders.
- Eagerness to learn and adapt.