Senior Data Scientist

Quick Facts

• On-site in Malmö, Sweden
• Working language: English (written and spoken)
• Role: Senior Data Scientist

Description

You will own end-to-end data science work for a privacy-sensitive, crowd-sourced location-finding network, focusing on large-scale spatiotemporal datasets where geography and time intersect. You will find signal, build models, and turn them into decisions without compromising privacy. You will collaborate with engineering to hand off reproducible, efficient prototypes from exploratory work through production.

Responsibilities

• Own analyses and models from the first exploratory question through production handoff
• Adapt quickly to changing priorities and rewrite plans as requirements and reprioritizations evolve
• Set problem-solving direction and push back when goal framing looks wrong
• Clean noisy, irregular raw sensor data; combine sources and derive new signals that hold up on real data
• Develop models that behave correctly on real-world data and explain outcomes clearly
• Work closely with engineering teams to scale prototypes into production services
• Explain complex work clearly in writing and in person; convert patterns into actionable metrics for non-technical stakeholders

Requirements

• Hands-on data science experience where your work reached production and was used; owned it end-to-end
• Strong geospatial fundamentals: geometry operations, spatial joins, spatial statistics, and coordinate reference systems
• Experience with GeoPandas, Shapely, or PySAL
• Experience cleaning raw sensor data, combining sources, and deriving robust signals
• Proven modeling range: scikit-learn or XGBoost for predictive work; Prophet or ARIMA for forecasting; use PyTorch for deep learning when needed
• Experience with spatial databases and distributed spatial processing (e.g., PostGIS, Apache Sedona)
• Experience with discrete global grid systems for hierarchical spatial indexing
• On-site availability in Malmö, Sweden (or willingness to relocate)
• English proficiency (written and spoken)

Benefits

• High autonomy with end-to-end ownership
• Close collaboration with engineering to deliver production-scale outcomes
• Emphasis on clarity, evidence-based decisions, and cross-team communication
• Commitment to fair treatment and reasonable accommodations
• Focus on accessibility and inclusion