Data Analyst - San Francisco
19 hours ago
Job description
Other locations: Canada, London, India & Australia.
Remote: OK
Job role:
As a data analyst, you will be responsible for compiling actionable insights from data and assisting program, sales and marketing managers build data-driven processes. Your role will involve driving initiatives to optimize for operational excellence and revenue.
Responsibilities:
- Ensure that data flows smoothly from source to destination so that it can be processed
- Utilize strong database skills to work with large, complex data sets to extract insights
- Filter and cleanse unstructured (or ambiguous) data into usable data sets that can be analyzed to extract insights and improve business processes
- Identify new internal and external data sources to support analytics initiatives and work with appropriate partners to absorb the data into new or existing data infrastructure
- Build tools for automating repetitive asks so that bandwidth can be freed for analytics
- Collaborate with program managers and business analysts to help them come up with actionable, high-impact insights across product lines and functions
- Work closely with top management to prioritize information and analytic needs
Requirements:
- Bachelors or Masters in a quantitative field (such as Engineering, Statistics, Math, Economics, or Computer Science with Modeling/Data Science), preferably with work experience of over 2-3 years (open to talk to freshers as well).
- Ability to program in any high level language is required. Familiarity with R and statistical packages are preferred.
- Proven problem solving and debugging skills.
- Familiar with database technologies and tools (SQL/R/SAS/JMP etc.), data warehousing, transformation and processing. Work experience with real data for customer insights, business and market analysis will be advantageous.
- Experience with text analytics, data mining and social media analytics.
- Statistical knowledge in standard techniques: Logistic Regression, Classification models, Cluster Analysis, Neural Networks, Random Forests, Ensembles, etc.
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