Telefonica Brasil

Team and Culture Description

Telefonica has global reach and presence with 317 million customers, owning network in 15 countries, operations in 44 countries and 650 roaming agreements worldwide, giving us real data on an incredibly large sample of our society.

The use of Data Science and Advanced Analytics with value creation from Big Data is one of Telefonica’s main priorities, enhancing our ability to anticipate customer needs and offer them relevant and personalized services, not only faster but at the right time, and also capturing external business opportunities in the sector.

Vivo Data Labs is a group dedicated to design and create solutions that leverage on Telefónica’s big data assets in Brazil to provide useful insights enlightening the space between the organization and our users, in order to improve propositions and business effectiveness. The team develops a range of algorithms and machine learning techniques, combining different datasets such as call center logs and records, traffic detail records (voice, data, SMS, and video), navigation records through company’s digital channels, and much more, as including traditional sources of telecom data.

The team works closely with various stakeholders inside Telefonica | Vivo and collaborates with other Data Labs groups from Global Telefonica and other Telefonica business operations.

Job Description

The roles and responsibilities of the candidate will be:                

• Analyze large, diverse and complex datasets efficiently and systematically, using data mining, machine learning and/or statistical techniques and tools
• Deliver innovative solutions that support decision-making and detect business opportunities, developing algorithms and sophisticated statistical models that provide recommendations regarding customer behavior segmentation, predictive modeling, network optimization, revenue assurance, real time campaigns, customer satisfaction, omnichannel solutions, data monetization, and more
• Document all the work done, completely and coherently
• Collaborate with Network Engineering, Product Management, Sales, Communication, Advertising, Revenue Assurance and IT teams to collect feedback/requirements and share results
• Work with IT architecture and infrastructure teams to improve data collection and pipeline
• Learn and understand a broad range of Telefonica | Vivo’s data resources and know-how, when and which to use
• Create and design dashboards with the aid of visualization tools and build metrics for presenting and tracking business  (business metrics)
• Perform basic testing on the prototypes, homologate solutions ready to be deployed to ensure they are correct and functional
• Communicate the rationale and the results of the analyses and solutions with a clear narrative that goes beyond the evidences of the numbers (storytelling)
• Manage numerous requests concurrently and strategically, prioritizing when necessary
• Provide technical leadership in analytics tools and data science techniques across the company, being recognized as an authority and reference on data value extraction in large scale computing environments
• Drive innovation in the top brand of Brazil’s telco industry, with the help of cutting edge technologies

The position is based in Sao Paulo-SP.

Basic Qualifications & Experience

• BA/BS degree or equivalent practical experience in a highly quantitative field such as Computer Science, Mathematics, Physics, Statistics, Engineering, Economics or other relevant applied field
• 2 years of work experience analyzing relevant patterns and insights extracted from data
• 2 years of experience delivering and presenting relevant results based on statistical models, data mining and/or machine learning techniques and tools
• 2-3 years of experience querying data (SQL skills)
• 2 years of experience working with languages that support statistical learning (e.g. Python, R, C/C++, Go, Scala, Java, MatLab, SAS)
• 2-3 years of experience (proficiency level) using Big Data applications: Hadoop/HDFS, Spark, MapReduce, Hive, Pig, MongoDB
• Solid base of statistical knowledge and computer science theory, with ability to create complex statistical models and algorithms
• Knowledge in back-end programming languages such as Java
• Ability to work on distributed platforms and interact with complex datasets in a business environment with large-scale
• Knowledge in data discovery and data visualization main tools and libraries
• Excellent verbal and writing English skills and fluent Portuguese
• Proactive, organized, practical and solution oriented. A sense of humor, personal integrity, humility and an appreciation for the power of true teamwork
• Abstraction capacity, creativity and natural curiosity for complex problem resolution
• Able to adapt to changes in a dynamic work environment
• Ability to handle multiple tasks, meet deadlines in a dynamic/changing environment, as well as to set priorities in a complex team and work environment (functional and regional reporting lines)

Preferred Qualifications & Experience

We are looking for rock star data scientists with various areas of experience and we believe there is no single ideal profile. Vivo Data Labs has room for a very diverse skill sets that could add value to the team:

• MS and/or PhD in Natural Science, Engineering, Computer Science or other relevant quantitative & applied field.
• Solid background and experience in one or more of the following areas: machine learning, natural language processing, signal processing, pattern recognition, recommender system, artificial intelligence, social network analysis, data mining, statistics and/or discrete optimization techniques, time series analysis and modeling
• Experience in software tools: pandas, numpy, scipy, stats, scikit-learn, NTLK, gensim, lm, theano, numba, luigi, airflow
• High level of Spanish proficiency
• Experience in the telecommunications industry or basic understanding of the mobile industry
• Experience working as data scientist in financial services, transport, SEO or biotechnology
• Experience working on projects regarding: Customer Lifetime Value Modeling; Predictive Modeling for Churn (including Survival Analysis), Acquisition, Upsell and Cross Sell; Credit Score Modeling; Customer base segmentation - high level and multivariate cluster analysis
• Knowledge or experience in building ETL
• Experience with Deep Learning
• Analytical mindset and ability to see the big picture
• The ability to distill problem definitions, models, constraints from informal business requirements; and to deal with ambiguity and competing objectives
• Consulting experience
• Notable achievements in machine learning competitions
• Relevant technical knowledge on fields such as: database systems, fundamentals of cloud computing and virtualization, storage systems, distributed, parallel and heterogeneous computing, computer architecture, networking, etc.

Contact info: paula.fadulfreitas@telefonica.com