Data Scientist, Commercial Analytics (R-19743)

The Data Scientist, Commercial Analytics will leverage advanced analytics, machine learning, experimentation, and predictive modelling to identify growth opportunities, reduce customer churn, optimize territory and account planning, and support strategic decision-making across the commercial organization.

This role works with large-scale customer, sales, product usage, and financial datasets to develop scalable analytical solutions that influence key business outcomes. It aligns closely with commercial priorities such as Annual Recurring Revenue (ARR) growth, retention, expansion, customer lifetime value, sales effectiveness, and portfolio optimization.

Responsibilities:

• Develop, deploy, and maintain machine learning models that support Customer churn prediction, Upsell and cross-sell propensity scoring, Whitespace opportunity identification, Customer lifetime value prediction, Renewal risk assessment and Sales forecasting.
• Apply advanced statistical techniques to identify key drivers of customer behaviour and commercial performance.
• Design and evaluate experiments to improve acquisition, retention, and expansion outcomes.
• Build analytical frameworks to improve Sales and Annual Recurring Revenue (ARR), Customer retention, Expansion revenue and Sales productivity.
• Conduct deep-dive analyses into customer, product, and sales performance.
• Create segmentation models that support territory design, account planning, and resource allocation.
• Partner with senior leaders across Sales, Customer Success, Marketing, Finance, Strategy and RevOps to identify growth opportunities.
• Translate complex analytical findings into actionable business recommendations.
• Present insights and recommendations to executive stakeholders.
• Identify opportunities to automate and scale analytical processes.
• Evaluate and implement innovative analytical methodologies and AI-driven solutions.
• Contribute to the development of best practices, modelling standards, and reusable analytics assets.
• Support the integration of GenAI and advanced analytics into commercial workflows.
• Collaborate with data engineering teams to improve data quality and accessibility.
• Build scalable data pipelines and analytical datasets.
• Maintain documentation and governance standards for analytical models and processes.

Essential Skills and / or Certifications:

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering or a related quantitative field.
• At least 8 years of experience in Data Science, Advanced Analytics, Commercial Analytics, Revenue Analytics, Marketing
• Analytics or related disciplines, with 4 years in a senior/scientist capacity
• Proven track record of delivering analytics solutions that drive measurable business impact.
• Demonstrated ability to work effectively in cross-functional environments.
• Strong proficiency in Python or R.
• Advanced SQL skills with the ability to work across large and complex datasets.
• Experience building machine learning and predictive analytics solutions.
• Strong knowledge of Classification models, Regression techniques, Clustering and segmentation, Time-series forecasting,
• Statistical testing and Experimentation methodologies
• Experience with cloud-based analytical platforms namely as Databricks, Snowflake, Redshift or BigQuery.
• Experience developing visualizations in Power BI or Tableau.
• Strong commercial acumen with the ability to connect analytical outputs to business outcomes.
• Excellent stakeholder management and influencing skills.
• Strong business storytelling and communication capabilities.
• Proficiency in Microsoft Office Suites Skills
• Show an ownership mindset in everything you do; be a problem solver, be curious and be inspired to take action, be proactive, seek ways to collaborate and connect with people and teams in support of driving success.
• Continuous growth mindset, keep learning through social experiences and relationships with stakeholders, experts, colleagues and mentors as well as widen and broaden your competencies through structural courses and programs.
• Where applicable, fluency in English and languages relevant to the working market.