What you'll do
- Develop analytical and machine-learning solutions to improve member acquisition, engagement, retention, and lifetime value.
- Build segmentation, propensity, recommendation, uplift, and optimization models for loyalty use cases.
- Design and analyze experiments, including test-and-control frameworks and causal-impact measurement.
- Translate business questions into rigorous analytical approaches and actionable recommendations.
- Partner with product managers, engineers, analysts, marketers, and other data scientists to define priorities and deliver production-ready solutions.
- Establish success metrics and measurement frameworks for loyalty initiatives.
- Communicate findings clearly to technical and non-technical audiences, including senior stakeholders.
- Promote high standards for data quality, reproducibility, model governance, and responsible use of customer data.
- Contribute to the evolution of the organisation's loyalty data platform and data products.
- Significant experience in data science, advanced analytics, or a closely related quantitative field.
- Strong programming skills in Python and SQL.
- Practical experience with statistical modelling, machine learning, experimentation, and causal inference.
- Experience working with customer, marketing, CRM, membership, rewards, or personalization data.
- Ability to work with large-scale, complex, and evolving data environments.
- Strong product sense and the ability to connect analytical outputs to customer and commercial outcomes.
- Excellent written and verbal communication skills.
- A collaborative, pragmatic approach and a track record of delivering outcomes through cross-functional partnerships.
Requirements
- Experience with loyalty, travel, marketplaces, subscriptions, rewards, or customer lifecycle analytics.
- 8+ years of experience in a similar analytical role.
- Experience developing models that influence customer journeys or marketing decisions.
- Experience taking models from prototyping through deployment, monitoring, and iteration.
- Graduate degree in statistics, computer science, operations research, economics, mathematics, or a related quantitative discipline.
Benefits
· Competitive salary and performance-based bonuses.
· Comprehensive insurance plans.
· Collaborative and supportive work environment.
· Chance to learn and grow with a talented team.
· A positive and fun work environment.
Skills Required
- Significant experience in data science, advanced analytics, or a closely related quantitative field
- Strong programming skills in Python and SQL
- Practical experience with statistical modeling, machine learning, experimentation, and causal inference
- Experience working with customer, marketing, CRM, membership, rewards, or personalization data
- Ability to work with large-scale, complex, and evolving data environments
- Strong product sense and ability to connect analytical outputs to customer and commercial outcomes
- Excellent written and verbal communication skills
- Collaborative, pragmatic approach and track record of delivering outcomes through cross-functional partnerships
- Experience with loyalty, travel, marketplaces, subscriptions, rewards, or customer lifecycle analytics
- 8+ years of experience in a similar analytical role
- Experience developing models that influence customer journeys or marketing decisions
- Experience taking models from prototyping through deployment, monitoring, and iteration
- Graduate degree in statistics, computer science, operations research, economics, mathematics, or a related quantitative discipline
What We Do
Prescience is a Danish SaaS company that provides collaborative supply-chain execution software for industrial businesses. Its platform helps customers and suppliers plan production, track manufacturing progress, monitor execution, manage capacity, improve quality assurance, collect as-built documentation, and coordinate inventory and inbound and outbound logistics. By integrating supplier data in real time, Prescience delivers visibility, risk detection, and operational control across dispersed global supply chains.



.png)





