Big Data Principle Professional

• Design, build and implement outstanding, robust, actionable analytical business solutions makes use of big data including structured data (ex: Teradata based customer database, large analytical table, offers response) and unstructured data (social media interactions, network transaction, survey response, web browsing, customer foot-falls) to identify the best practice for big-data use cases in line with business objectives.
• Communicating complex analytical and technical concepts to a business audience.
• Identify key questions that can be answered with data and advanced analytics, leveraging unstructured, noisy and big data where appropriate.
• Formulate the analytical approach and data acquisition strategy to answer the questions identified
• Go through all phases and iterations required to deliver analytical solutions, from data exploration, cleansing or feature creation to building models and creating compelling visualizations, making sure the solution answers a relevant business problem
• Conduct data discovery and exploration for unstructured data and perform the standard preprocessing procedures.
• Visualize and tell stories with data, take decision based on this data and present results to business areas
• Develop and deliver model's outcome and performance reporting, critical for tracking and managing the business, including weekly, monthly, and quarterly operational metric reports.
• Identify new datasets to be capturedto enrich the drive analytical attributes.
• Design, develop and maintain algorithms to extract relevant information from big amounts of data, scalable software systems and algorithms to clean, standardize, and analyze raw data.
• Evaluating and differentiating techniques, tools and approaches to Deep Learning problems
• Explanation and documentation of analytical model's techniques and results.
• Development of SQL to enhance existing stored procedures.
• Maintain already developed automated end-to-end flow.
• Write clean, scalable and fast performing code according to guidelines and quality standards (solid principles, code readability, pattern use) and review other developers' code.
• Drive the implementation of the analytical strategy and work with functional business leaders to build scalable processes and metrics.
• Pursue the generation of common components and best practices of big-data use cases and fostering the reuse of big-data technology and platform.
• Participate in “make” vs. “buy” decisions from a technical point of view concerning technology.
• Demonstrated track record of prototyping and launching industry leading big-data solutions.
• Spearhead innovation in the application of data analytics to highlight the potential use cases for external big-data monetization.
• Participating in learning and training initiatives for the wider analytics teams

• Strong Background in Predictive modelling , Machine Learning, Data mining and Artificial Intelligence, including: Support vector machines, Bootstrap aggregating / bagging, Cluster analysis, Cascading classifiers, Decision trees, Time series analysis & time series forecasting, Boosting, Factor analysis, Structural equation modelling, Item response theory, Markov chains, Voronoi diagrams, Neural networks, Genetic algorithms, Data visualization, Bayesian modelling, Multivariate regression, Logistic regression, etc.
• Solid understanding of data management, implementation of machine learning algorithms and various statistical modelling techniques.
• Good understanding of the science behind machine learning algorithms (supervised and unsupervised algorithms), statistical and optimization techniques.
• Ability to attach complex business questions with data and curiosity to dive deep, identifying the root cause and “so what” rather than just the trends.
• Thrive in an environment that is tasked with providing data-driven decision support and business intelligence that is timely, accurate and actionable.
• Eeffective prioritize projects, manage multiple competing priorities simultaneously and drive projects to completion under tight deadlines.
• Experience in data mining using databases in a business environment with large-scale, complex datasets.
• Effectively communicate with both business and technical teams.
• Think big, understand business strategy, provide consultative business analysis, and leverage technical skills to create insightful, effective BI solutions.
• Hands on experience in SQL, PL/SQL, Excel, Linux and OLAP, SAS, Scala, R, Python (data extraction, manipulation, data insights).
• Experience with data presentation and visualization tools (Shiny, Tableau, Power BI, JasperSoft, QlikView, MicroStrategy, Business Objects) and able to translate complex insights in a story telling dashboard.
• Good understanding of Hadoop Ecosystem components, Hadoop MapReduce framework, streaming processing frameworks.
• Experience of Hadoop-based analytical tools (Mahout, Hive, Pig. RHadoop, MOA, Jabatus, Alpine, etc.)
• Proficiency in query languages like Hive and NoSQL databases like HBase.
• Graph analysis, Geo-spatial analysis and NLP is plus.
• (PhD is a plus).

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