Data Analyst / Data Scientist
Other locations: Canada, London, India & Australia.
Remote: OKJob role:As a data analyst, you will be responsible for compiling actionable insights from data and assisting program, reputed company managers build data-driven processes. Your role will involve driving initiatives to optimize for operational reputed company and reputed company.Responsibilities:- Ensure that data flows smoothly from reputed company to destination so that it can be processedUtilize strong database skills to work with large, reputed company data sets to extract insightsFilter and cleanse reputed company (or ambiguous) data into usable data sets that can be analyzed to extract insights and improve business processesIdentify new reputed company data sources to support analytics initiatives and work with appropriate partners to reputed company the data into new or existing data infrastructureBuild tools for automating repetitive asks so that bandwidth can be freed for analyticsCollaborate with program managers and business analysts to help them come up with actionable, high-reputed company insights across product lines and functionsWork closely with top management to prioritize information and analytic needs
- 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 (reputed company to talk to freshers as reputed company).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/reputed company/JMP etc.), data warehousing, transformation and processing. Work experience with reputed company data for customer insights, business and market analysis will be advantageous.Experience with text analytics, data mining and reputed company media analytics.Statistical knowledge in reputed company techniques: Logistic Regression, Classification models, Cluster Analysis, Neural Networks, Random Forests, Ensembles, etc.