Analytics Manager

Job Description: Leading a team of analysts to support GTM and Sales pillars. Empower and guide your team to study the intersection between customer behavior, sales performance and product usage to make data-driven decisions. Define, evaluate, and monitor success metrics, root causes changes, and proactively surface insights to senior leaders. Serve as a strategic thought partner to key stakeholders across the org, prioritizing and executing on analytical investigations and building data products. Collaborate with data science on experimentation and developing models that help us predict customer engagement. Effectively communicate analytical findings to non-technical audiences; mentor and guide your team on strategic communication. Day to day duties include managing projects against appropriate timelines, guiding analysts through the execution of projects in their roadmap and communicating findings to stakeholders and peers. This will require daily use of technical skills and familiarity with our data platforms.

Requirements: Bachelor’s degree or foreign degree equivalent in Statistics, Economics, Analytic Business Administrations, or related field and five (5) years of progressive, post-baccalaureate experience in Business, Marketing, or Product Analysis, or the job offered or related role. Alternatively, the employer will accept a master’s degree or foreign degree equivalent in Statistics, Economics, Analytics, Business Administration or related field and three (3) years of experience in in Business, Marketing, or Product Analysis, or the job offered or related role. Any suitable combination of education, experience, or training acceptable.

Experience and/or education must include:   

1. People Management- managing a team of analysts/data scientists;

2. Metrics - Creating documents and presentations summarizing analytic insights and identifying opportunities for growth;

3. Sales productivity and experience with Salesforce;

4. Forecasting: Experience with forecasting tools such as Tableau, Salesforce and Clari;

5. SQL - Querying and transforming data for automated and ad-hoc reporting using SQL and R and/or Python;

6. Working in a big data environment with unstructured or semi-structured data;

7. Statistical modeling - Applied use of statistics/statistical concepts including sample size, significance, statistical testing, non-parametric testing, hypothesis design, metric definitions, power analysis, confounding effects; and

8. Familiarity with datalakes and data warehouses.