Senior Data Analyst

As a hybrid analyst-data scientist, youll partner closely with Customer Success, Professional Services, Training, and Support teams to deliver both advanced modelling, and foundational analyses. Youll uncover patterns, build models, and translate complete findings into clear, actionable recommendations that influence senior leadership decisions and improve Enterprise customer outcomes.
If you thrive on uncovering patterns in data, influencing high-level business strategy, and translating insights into measurable customer outcomes, this role is for you.
Responsibilities:
Work as part of a small Customer Success Insights and Operations team that leads data analysis, predictive insights and operational support to a growing CS team dedicated to the success of our customers.
Deliver actionable analyses and insights across key Enterprise CS OKRs, initiatives and growth experiments - leading 2-3 strategic projects per Quarter. These projects could be anything from churn analysis, expansion-likelihood modelling, behaviour clustering, to NPS sentiment and customer success plan performance.
Build, test and maintain predictive and statistical data models that identify risk, quantify revenue opportunities and prioritise CS actions at scale.
Support the creation of dashboards with our BI team that track customer health, renewal risk, and engagement impact across our enterprise accounts.
Design and analyse growth and expansion experiments to identify and scale opportunities for product adoption and usage expansion.
Partner with CS leadership to support MBRs/QBRs, operational reviews, and executive reporting.
Drive cross-functional collaboration with Product, Sales, and Marketing to ensure alignment on customer insights and opportunity areas.
Respond to specific internal CS requests to support customer retention and growth plays, delivering 5+ high-value analyses monthly that fuel CS OKRs and strategic decision-making.
Leverage statistical and analytical tools (e.g., SQL, Python, statistical / ML libraries as well as BI tools like Qlik) to model data, test hypotheses, and communicate recommendations with clarity and impact.
Explore and help operationalise the use of AI-tools like ChatGPT and Google Gemini to support analysis velocity, identify patterns and automate insights generation.
Champion data quality and governance, ensuring reliable and scalable data pipelines in partnership with MIS and Data Engineering teams.
Advise on KPI design and performance tracking for Enterprise CS metrics including NRR, GRR, churn, and adoption health scores.