Science Manager

About our client

Our client is a fast-growing, mission-driven deep tech venture utilizing complex proprietary datasets to unlock previously unavailable predictive insights. Operating at the intersection of high-performance physics, complex environmental signaling, and large-scale software infrastructure, they provide actionable, global-scale intelligence to multi-billion-dollar commercial sectors worldwide.

The Luxembourg-based team (comprising Software, Data, Machine Learning, and Science) bridges heavy scientific R&D with commercial product engineering. This cross-functional division is responsible for scaling advanced mathematical and physical models into high-velocity production systems that drive global risk management and resource resilience.

The Mandate

The Science team consists of highly accomplished, senior-level academic specialists, data scientists, and physicists. The primary challenge is cultural and operational: transitioning this brilliant team away from open-ended academic exploration and into a disciplined, iterative commercial startup cadence. The goal is to build an operating rhythm that allows the team to deliver scalable, production-ready models within shorter, predictable engineering timelines without compromising scientific validity.

Core Pillars & Responsibilities

• People & Performance Management: Direct, mentor, and guide a highly educated, diverse, and cross-functional team of senior data scientists and physicists. Establish clear key performance indicators and structured career growth paths.

• Technical Sounding Board: Act as an expert advisory layer. Review algorithm designs and statistical physical models, offering critical structural feedback to ensure their viability for software engineering integration.

• Commercial Delivery Orientation: Shift team workflows from absolute academic perfectionism to high-velocity commercial delivery (Minimum Viable Product mindset).

• Cross-Functional Collaboration: Align scientific outputs with internal Data, Machine Learning, and Software Engineering leads to seamlessly productionalize models.

Candidate Profile & Requirements

Must-Have

• Commercial / Private Sector Experience: A proven track record operating within a corporate product, commercial software, or fast-paced startup environment. Purely academic or public-sector research profiles cannot be considered for this mandate.

• People Management Experience: Demonstrated history of formal team management, including setting operational expectations, performance management, resolving friction, and developing senior technical personnel.

• Technical & Mathematical Foundation: Advanced degree (Master's or equivalent) in Environmental Physics, Geoinformatics, Data Science, Applied Mathematics, or a related deeply statistical physical science domain.

• Domain Literacy: Strong conceptual understanding of large-scale environmental datasets, spatial/temporal data processing frameworks, and physical land-surface interactions to command the immediate respect of a highly technical team.

Nice-to-Have

• PhD: A doctorate in Environmental Physics, Applied Mathematics, Data Science, or a related statistical physical science field.

• Cloud Architecture Familiarity: Experience collaborating on models deployed within cloud-based infrastructure (e.g., AWS, GCP).

• Research Footprint: A proven track record of peer-reviewed publications or notable applied research in spatial analytics or physical modeling.

If you don't correspond to 100% of the must-have requirements, but believe your profile fits well to this role, we still want to hear from you!