Senior Data Analyst, Revenue Analytics

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Requirements:

• Experience in data science / advanced analytics / statistical modelling experience, ideally within Sales/Marketing/Finance or other commercial parts of the organization
• Working knowledge of workhorse machine learning algorithms (particularly supervised learning using Python), and their typical use cases in a commercial organization
• Insatiable curiosity to proactively pose deeper questions, not be satisfied with superficial answers, and dig into vast granular data to uncover conclusive answers analytically
• Strong proficiency in SQL (in Snowflake) to manipulate data easily
• Demonstrated ability to craft precise analytical questions from ambiguous business problems, and roll up the sleeves to address them
• Understanding of causal inference methods to be able to utilize quasi-experimental or other econometric techniques to determine causality among different initiatives (essentially, finding out “what works,” and what does not, especially around Sales & Marketing questions)
• An attitude to “get stuff done” particularly around being actively involved with data cleaning and quality, building reports or answering important business questions (being a full-stack data professional)
• Top-Tier communication skills in English, to distill complex mathematical models, concepts and findings into simple, intuitive words and charts for senior commercial leaders

Responsibilities:

• Initially focusing on developing and maintaining KPIs and dashboards with the rest of the team, to monitor metrics and uncover insights as the primary responsibility for the first ~3-6 months (also to understand the business effectively)
• Liaising productively with Data Analysts, Analytics Engineers and the business teams/owners to ensure that data products, analysis & statistical models built are not only technically sound but highly effective and widely useful
• Building predictive models around different business areas and problems within Revenue Analytics : Lead Scoring, Marketing Attribution, Customer Churn Prediction, Optimizing Sales Processes, Building Books of Business
• Maintaining the data pipelines and statistical models, and also continuously calibrate existing models to account for changing business conditions and/or customer behaviour
• Using causal inference and quasi-experimental techniques to dig into historical & current data to statistically identify which initiatives worked and what was the lift they produced
• Owning important business questions analytically and technically, creating new knowledge as well as raising the standards for advanced analytics work.