Compañía

S&P GlobalVer más

addressDirecciónCiudad de México
CategoríaInvestigación y ciencia

Descripción del trabajo

Data Scientist – S&P Global Commodity Insights
S&P Global is looking for a Data Scientist who will part of our Data Science and Modelling team, the ideal candidate should be highly motivated and goal-oriented, with an encouraging attitude of working in a very dynamic work environment with a wide range of stakeholders and functional teams.

Job summary:


As a Data Scientist at S&P Global, you will apply your advanced analytics skills to transform our vast datasets into tangible business value. You will slice and dice data, analyse information, communicate your findings and collaborate on product development. Outcome of your work will serve multiple business purposes, from commercialized software solution development, driving internal business decisions, to consulting external clients.

Responsibilities:


• The candidate will drive business decisions based on the insight obtained from the data.
• Strong problem-solving skills with an emphasis on product development.
• Selecting features, building and optimizing machine learning techniques to improve the accuracy of new products.
• Analyse data for trends and patterns, understanding insight from data with a unbiassed mindset.
• Implement different analytical methodologies to help solve various problems related to the energy industry.
• Close collaboration with software developers and machine learning engineers in the implementation of analytical models into production or commercialization.
• Develop and utilize algorithms to perform error analysis to improve model uniformity and accuracy.
• Communicate your findings to both technical and non-technical stakeholders.
• Comfort working in a very dynamic, research-oriented team with several projects under construction in parallel.

Required Qualifications:


• Advanced degree in a highly quantitative field: Computer Science, Machine Learning, Geology, Statistics, Mathematics, etc.
• Proficiency with data mining, knowledge of probability theory and advanced statistical techniques.
• Experience with regression analysis (beyond linear regression), supervised learning, unsupervised learning or time-series analysis.
• Experience with scientific scripting languages (e.g., Python, R, Matlab)
• Experience accessing and manipulating data in SQL database environments.
• Excellent communication and presentation skills. The candidate must be able to effectively communicate with management, reservoir characterization teams, engineering teams, clients, and other stake holders.

Preferred Qualifications:
• Recent work or internship experience in an advanced data analytics role.
• Experience with object-oriented programming (e.g., C#/C++, Java).
• Knowledge of deep learning and related toolkits: Tensorflow, PyTorch, Keras, etc.
• Knowledge of the energy industry, understanding the different challenges and potential solutions.

Location: Mexico Remote, Colombia remote

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Equal Opportunity Employer

S&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race/ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other status protected by law.  Only electronic job submissions will be considered for employment.  

If you need an accommodation during the application process due to a disability, please send an email to: **************@spglobal.com and your request will be forwarded to the appropriate person.  
 
US Candidates Only:  The EEO is the Law Poster http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf describes discrimination protections under federal law. 

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202 - Middle Professional (EEO Job Group) (inactive), 20 - Professional (EEO-2 Job Categories-United States of America), BSMGMT202.1 - Middle Professional Tier I (EEO Job Group)
Refer code: 989693. S&P Global - El día anterior - 2024-01-04 13:34

S&P Global

Ciudad de México
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