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Job Location | Delhi |
Education | Not Mentioned |
Salary | Not Disclosed |
Industry | Media / Dotcom / Entertainment |
Functional Area | General / Other Software |
EmploymentType | Full-time |
Data ManagementBuilding pipelines from different data sources to prepare core dataset for the engine to work onData prep and cleanup (missing data, outlier detection, duplications) from internet scale digital advertisement/ survey response dataData Integration (merge, join, subsetInsight GenerationMethodology writeups, interpretation of resultsApplications and generalization (Use cases, automation and scalability evaluation) POVs, 1- pager, vendor evaluation and assessmentTechnical ProgrammingCoding languages (Python is must, R is good to have) and Google tools (BigQuery, Airflow, GCP) Comfortable with data retrieval and processing with SQL and NoSQLSolid understanding of relational and non- relational database technology, data pipelineUnderstanding of code version management system A bit about yourself: Preferred: Knowledge of popular ad serving technologies and supporting analytical and research tools (Doubleclick, Atlas, comScore, Compete, etc. ) What you can expect from EssenceEssence s mission is to make advertising more valuable to the world. We do this by employing the world s very best talent to solve some of the toughest challenges of today s digital marketing landscape. It s important that we hire people whose values reflect those of our own: genuine, results- focused, daring and insightful. As an Essence employee, we promise you a workplace that invests in your career, cares for you and is fun and engaging. We believe these factors create a workplace where you can be yourself and do amazing work. A degree in Engineering (Computer science is a plus) or equivalent fieldAt least 4- 6 years of industry experience in a data- focused roleExperience with managing multiple large data sources (structured or not) Ability to use the data to create a story and help create new analytical products or change an existing product in a way that improves company goals and metrics. Knowledge of the data science lifecycle - acquiring data from different sources, exploratory data analysis, scheduling tasks, creating and optimizing modes including validation and interpretation, and finally onward communication of results in a clear and succinct fashion. Hands- on Experience with programming languages (SQL, Python ) , statistical software packages (R, Python SciPy) would be a plus. Baseline understanding of all the five core competencies: programming, statistics, machine learning, data munging, and data visualization. Ability to communicate your findings effectively to non- technical audiences.,
Keyskills :
logistics delivery hubs management adserving datascience datamunging dataanalysis machinelearning dataintegration codinglanguages digitalmarketing nvent ymanagement expl at ydataanalysis vend evalua