SPECTRUM Performance is looking for a Data Scientist in None – Apply Here!
Spectrum Reach (www.spectrumreach.com) grows businesses of all sizes with custom, multiscreen advertising solutions, backed by the power of TV, data, innovation, community experts, and unforgettable creative.
We offer a hybrid work model at Spectrum Reach.
The Data Scientist is a talented and experienced problem-solver, using analytic techniques to answer questions, identify opportunities, and make recommendations for our business. The scope of assignments will be broad, covering our client experience, operational efficiency and effectiveness, and financial and subscriber performance. They will be asked to perform the full life-cycle of data analysis, and to contribute to each step in a hands-on fashion.
Major Duties And Responsibilities
• The Data Scientist will perform the full life-cycle of data analysis from definition of questions / issues to presentation of results and recommendations. This work will be carried out with close direction and dialog with the Director of Data Science and other Data Scientists, and with daily interactive participation with business and data SMEs across the organization.
• This life-cycle of data analysis includes hands-on iterative execution of these steps:
• Definition: Dialog and interview with key business stakeholders on the issues and questions to answer during analysis. Provide direction and suggestions based on experience in order to clearly articulate the business questions and manage the scope of analysis. Document an analysis charter that outlines scope and goals of analysis.
• Data Research: Using a variety of techniques, discover and research the available sources of data. These techniques should include discussion with data and system SME’s, use of data management assets such as documentation, database catalog tables, and data models, and the use of data profiling and exploratory data analysis.
• Data Preparation: Using SQL and DB techniques assemble the available data, performing necessary integration, summarization, and transformation of data in order to support the analysis goals.
• Data Analysis: Using basic and intermediate techniques such as crosstab analysis, charting, and geographic mapping, examine the data in the context of the issues at hand. Identify when more advanced techniques are required, such as statistical analysis, regression modeling, etc., and participate in the use of these techniques.
• Synthesis: Draw conclusions and make recommendations based on the observed patterns in the data. By sharing these conclusions for feedback and refinement, continuously improve the veracity and utility of the findings. In close dialog with the business SME’s, identify potential areas for further analysis, measurement, or business process change.
Skills/Abilities and Knowledge
• Work closely with data and business issues and hands-on data analysis.
• Experience as visible influencer using data on business issues in a complex organization.
• Ability to quickly learn issues and business context of an assignment.
• Expert-level data manipulation skills in a relational-database environment:
• Expert-level SQL on a variety of DB platforms.
• Experience using DB database dictionary/catalog and data profiling techniques in order to learn about and assess data.
• Experience reading data models (ERDs). Knowledge of data modeling issues, such as normalization, star-schema designs, relationships, and keys.
• Effective and experienced in engaging data and system SMEs in dialog to learn about data and interpretation of it.
• Expert-level skills using analysis techniques:
• Design and use of basic analysis and description techniques, such as crosstabs (tables of sums and proportions), charts, and figures.
• Design and use of intermediate analysis such as regression analysis, decision trees, time series analysis, principal component analysis
• Design and use of Linear Programming tools
• Experience with the key issues involved when applying intermediate analytic techniques to data. Ability to describe the issue, an example of professional experience where the issue applied, and what the remedy was. Issues should include:
• Confounding correlations in data.
• Noise / random variation versus a meaningful trend or response.
• Data quality issues
• Tools Experience should include:
• Expert-level SQL
• Expert-level Excel
• Familiarity with R highly desired. Experience with one of R, SAS, Minitab, etc. required.
• Familiarity with Python scripting highly desired
• Desired Qualifications:
• Ability to teach and influence others in all of techniques used, both data and analytic.
• Exposure to advanced analytic techniques such as Gradient Boosted algorithms and geo-spatial analysis.
• Experience in the telecommunications / entertainment industry.
• Experience designing basic experiments for measurement of impact, in a business operations context.
• Experience with Cloud based architecture, like AWS
Related Work Experience
• Data manipulation and statistical modeling
• experience as a Scientist, Consultant,
• Architect, DBA, or Engineer
• Programming experience
Bachelor’s degree in computer science, statistics, operations research or equivalent combination of education and experience.
• Extensive experience with data programming languages such as SQL/R/Python
• Extensive experience with large data sets or Big Data
BDA303 328860-2 328860BR