Data-driven intelligence applied & delivered
As a data-driven, strategic communications agency, we believe our clients become stronger when they leverage the wealth of information consumers provide every day. At Targetbase, we integrate technology, analytics, creative, and strategy to create a better consumer experience, which improves consumer engagement and drives a more profitable outcome for clients.
Job Scope & Purpose
The Data Scientist is a member of the Strategic Business Analytics team and participates in an analytic, novel insight-generating role. He/she is expected to develop powerful analytic solutions that address clients’ real marketing problems – this is not a data collection, cleansing / merging / data-creation and algorithm-implementation position. In addition, he/she provides novel analytic insights in relation to the client’s business domain and needs, not the technology behind the data collection, algorithm implementation, etc.
The Data Scientist is a team player, who is flexible, creative, and eager to learn and teach others. He/she is an exceptional data researcher with a desire to create business insights that drive decision-making and positively impact brands’ business results.
He/she is passionate about extracting the signal from the noise, and for merging and transforming rich multidimensional (and often imperfect) datasets into insight that improves decisions. This position requires the spirit of an entrepreneur who is outcomes- and customer-focused, and who thrives in a dynamic environment with lots of personal responsibilities and limited formalities.
- Delves into huge, noisy, and complex real-world behavioral data to produce innovative analysis and new types of consumer or business measures around consumer behaviors and engagement and business performance.
- Uses creativity to find hidden gems that improve the understanding of consumer behaviors, brand-engagement and digital-media patterns to drive actionable business decisions.
- Collaborates with teammates to solve business problems, using a broad spectrum of data science tools, packages and visualization techniques.
- Disseminates knowledge of advanced analytics / machine learning and big data-application techniques within the organization and coaching junior analysts who want to move into this area.
- Able to offer thought leadership in the form of white papers and / or conference speaking.
- Builds analytical models using statistical, machine learning and data mining methodologies.
- Executes standard exploratory and ad hoc data analysis. Interprets and presents results using tools such as PowerPoint, Excel or Tableau.
- Implements and evaluates business metrics.
- Applies cleansing, discretization, imputation, selection, generalization etc. to create high quality features for the modeling process.
- Uses big data, relational and non-relational data sources to access data at the appropriate level of granularity for the needs of specific analytical projects. Maintains up to date knowledge of the relevant data set structures, access levels and ownership information.
- Utilizes Hadoop, SQL and NoSQL languages, tools and technologies to extract and process data for analytical needs. Develop data processing pipelines.
- Collaborates with software development teams to operationalize analytical solutions and create analytical/business intelligence products.
- Participates in the analysis and formalization of the business problems.
- Constant learning new analytical algorithms, tools and technologies in the big data ecosystem to build efficient and scalable analytical solutions.
- Consistent exercise of independent judgment and discretion in matters of significance.
- Other duties and responsibilities as assigned.
Experience & Education
- PhD in Computer Science, Applied Mathematics, Statistics, Econometrics, Quantitative Social Sciences, Industrial Engineering or related field.
- At least 1-3 years of experience depending on educational level and relevance.
- Demonstrations of investigating mathematical techniques relevant to consumer purchasing, engagement or media-consumption behaviors.
- Demonstrations of having written & tested code to operationalize these algorithms.
Knowledge & Ability
- Knowledge of analytics / machine learning, data mining and natural language processing algorithms with special emphasis to advanced algorithms like neural networks, SVM, random forests, hierarchical Bayes, gradient boosting machines, etc.
- Practical experience in predictive modeling including variable selection, data imputation, co-linearity diagnostics, factor analysis, variable interaction analysis, etc.
- Knowledge of at least one of the analytics languages / toolkits such as R, Python with analytical extensions, SAS, SPSS or Matlab.
- Knowledge of at least one programming / scripting language like Python, Scala, Julia, Ruby, or Java, C#, etc.
- Understanding of big data concepts and knowledge of big data languages/tools such as Hive, Pig, Mahout or Spark is a plus.
- Proven track record of conceptualizing, testing and implementation data mining & analysis techniques.
- Effective written and verbal communication and presentation skills.
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