Principal Analyst/ Head of CRM Analytics

What we are looking for someone who has experience with:

• Data Analysis and Interpretation: Understanding how to extract meaningful insights from data and making data-driven decisions.
• Business Intelligence: Using data to drive strategic business decisions and providing data visualization to communicate insights effectively
• Statistical Analysis: Proficiency in statistical methods and their application to decision making & analytics / data science problems
• Stakeholder management: Developing and implementing processes to align analytics and insight team backlog and priorities to business related objectives leveraging a cross functional and outcome driven mentality
• Experience in leading analytics and/or data science and/or insights teams, ideally at a head of level or senior IC 
• Experience in developing and implementing data-driven strategies to drive business growth and decision-making.
• Proven ability to use in using analytics and statistical methods to derive insights and create business value.
• Communication and leadership skills to effectively collaborate with cross-functional teams and senior stakeholders

What you'll be doing:

Impact:

• Support CRM team in their objectives with a strong focus on developing analytics capabilities, insights generation, decision science setup.
• Collaborate with Data Engineering and Reporting team to develop solutions that enables your team and develop a self-service mentality around data aimed to increase data literacy and focus analyst on more rewarding and forward-thinking work.

CRM Analytics team:

• Develop clear and compelling objectives for the area you lead, leveraging
  your understanding of company strategy and of the area you are supporting.
• Develop metrics frameworks and decision rules to enable better CRM objectives and 
  outcomes including the ROI analysis of the campaigns and customer segmentations
• Master our customer obsess superpower mapping how analytics, insights and data can 
  help improving retention, re-activation in an efficient way as well growing customer
  value like promotion types, size and timing
• Influence product teams with data driven hypotheses and lean value tree methodology
  as measures of success for example on early life AHA moments
• Promote the usage of the insights generation loop to keep generating them with 
  increased quality, turnaround, build credibility in your team and push towards self-
  service analytics

Team & Culture:

• Develop your team and build a top talent team. 
• Review team structure and redesign as required to ensure a scalable, effective and lean organization, promoting true collaboration across teams and clearly defining accountability 
• Constantly review skills of your team, tools and processes contributing to the continuous development of our data infrastructure and the data knowledge base 
• Promote a culture of 360 continuous feedback. 
• Be rigorous and consistent in assessing performance, recognizing outperformance and addressing underperformance, if necessary, by exiting individuals and making new hires to upgrade talent as required.  
• Create a succession plan for yourself beyond. Ensure a high level of talent retention among top performers. 
• Actively contribute and execute action to improve the analytics maturity of your area and the entire company 

Delivery focus:  

• Coordinate effort allocation within your team. 
• Coordinate cross functionally (data engineering, data capabilities, data science) to be sure majority of the effort is allocated to shared objectives and outcomes. 
• Balance constantly between quick wins and long-term work avoiding the stakeholder requests trap. 
• Manage key deliverables and timelines across teams. 
• Partner actively with the stakeholders, implementing processes and discipline (e.g. OKRs) to refresh the roadmap. 
• Partner closely with the Data teams (Engineering, Data Capabilities and Data Science) to shorten product development cycles, improve solutions available to your team, deliver the data enable company vision. 
• Foster a culture of innovation, encouraging ideation and experimentation rather than sticking to what works or the path of least resistance.  

Lead by example:   

• Be critical but fair always offering alternatives with a growth mindset. 
• Support your peers offering help and constantly promoting the evolution of the analytics and insights org