Data Analyst / Data Scientist

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.