Job Description
Data science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, chemometrics, information science, and computer science, including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, artificial intelligence, and high performance computing. Methods that scale to Big Data are of particular interest in data science, although the discipline is not generally considered to be restricted to such big data, and big data solutions are often focused on organizing and preprocessing the data instead of analysis. The development of machine learning has enhanced the growth and importance of data science. Data science utilizes data preparation, statistics, predictive modeling and machine learning to investigate problems in various domains such as healthcare, retail, marketing optimization, fraud detection, risk management, marketing analytics, public policy and agriculture. It emphasizes the use of methods such as machine learning that apply without changes to multiple domains. This approach differs from traditional statistics with its emphasis on domain-specific knowledge and solutions. Professionals in this field use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings. They are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to get/present results with dashboards.

Data Scientists convert business problems into an analytics solution using their consulting skills, business knowledge to analyze client business issues, formulate hypotheses and test conclusions to determine appropriate solutions methods. A solid foundation typically in statistics, modeling, operations research, computer science and applications, and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization. A data scientist is effective at deploying an analytics solution, thereby realizing business value. Whereas a traditional data analyst may look only at data from a single source – a CRM system, for example – a data scientist will most likely explore and examine data from multiple disparate sources. The data scientist will extract, transform, and combine all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. A data scientist does not simply collect and report on data, but also builds statistical models, determines what it means, then recommends ways to apply the data. Data scientists are inquisitive: exploring, asking questions, doing "what if" analysis, questioning existing assumptions and processes. Armed with data, modeling expertise, and analytical results, a top-tier data scientist will then communicate informed conclusions and recommendations across an organization's leadership structure.

A Data Scientist -Cognitive computing combines deep data and analytics skills with strong business acumen to solve business problem of Cognitive Computing Solutions. Expected to have expertise of Advanced Analytics techniques that are traditionally applied to structured data, as well as deep understanding of Natural Language Processing and Machine Learning techniques for unstructured content, so as to enable composition of holistic cognitive solutions. Is expected to be well versed in one of the following areas of Natural Language Processing, Image Processing, Video Processing, Voice Processing and Watson technologies

Required Technical and Professional Expertise

• At least 5 of years of experience as data scientist
• Experience in data mining and advance analytics
• Programming skills with Python or/and R and libraries like Scikit, NumPy or Pandas
• Knowledge in Machine and Deep learning frameworks like Tensorflow, CAFFE or Torch
• Knowledge in SPPS or/and SAS will be also valued.
• Experience in natural language processing techniques
• Exposure to Big data technologies as Hadoop, MapReduce, Hive, Spark, Impala…
• Ability to think analytically, quantitatively and creatively
• Advanced level of Spanish and English
• Proven record of leadership
• Ability to work effectively with people at all levels in an organization and also in a team environment
• English and Spanish fluent

Preferred Tech and Prof Experience

• Degree in Statistics, Mathematics, Physics, Quantitative Economics or Engineering
• Postgraduate studies in the area of Big Data, Machine Learning, Deep Learning, Business Analytics or Business Intelligence will be strongly valued.

EO Statement
IBM is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

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