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Data Scientist - Qualified Pipeline

Remote:

Full Remote

Contract:

Full time

Experience:

Mid-level (2-5 years)

Work from:

Brazil

Offer summary

Qualifications:

Bachelor’s degree in Computer Science, Math or Statistics preferred, At least 4 years of professional experience in a data-related role, Good communication skills, knowledge of Statistics, Machine Learning, and Predictive Analytics.

Key responsabilities:

Ensure data integrity and process structured/semi-structured data
Analyze large datasets, build predictive models, and propose business strategies
Deploy models, communicate results clearly, champion data-driven decisions
Apply statistical techniques, create data visualizations, collaborate efficiently
Maintain skills through education, stay updated on new technologies/tools

Data Meaning SME https://www.datameaning.com/

51 - 200 Employees

See more Data Meaning offers

Job description

Data Scientist

Location: Latam/US
Position Pay: Salary depends on experience

POSITION SUMMARY:

Data Meaning is a front-runner in Business Intelligence and Data Analytics consulting, renowned for our high-quality consulting services throughout the US and LATAM. Our expertise lies in delivering tailored solutions in Business Intelligence, Data Warehousing, and Project Management. As a Tableau Preferred Partner and the distinguished Alteryx 2020 Global Partner of the Year, we are dedicated to bringing premier services to our diverse clientele. Our strength lies in our global team of 95+ consultants, all working remotely, embodying a collaborative, inclusive, and innovation-driven work culture.

We are proactively building a pipeline for qualified Data Scientists to joinour team in the near future.

The IDEAL candidate will have experience with either Python or R, Power BI or Tableau, and excellent understanding of Data Analysis, Data Mining, Statistic Modeling and Big Data Infrastructure.

RESPONSIBILITIES:

Follow consistent practices to ensure data integrity and deal with imperfections in data.
Effectively process structured and semi-structured/unstructured data; proficiently integrate varied datasets
Work cross-functionally with development and engineering teams
Analyze large amounts of information to discover trends and patterns, draw conclusions and gain actionable insights.
Build predictive models; propose solutions and strategies for business problems
Apply the appropriate machine learning algorithm to data problems (e.g. supervised vs. unsupervised machine learning, clustering, ensemble methods, etc.), validate model results.
Work closely with architecture and engineering team to deploy models; communicate results in a clear and non-technical manner.
Interact with senior management and effectively communicate technical information to numerous audiences (e.g. senior management, client community, peers and junior associates)
Address challenges to analytic processes and influence others to change their approach and work collectively to implement new tools/techniques
Gain support from related stakeholders for analytic solutions and champion data driven business decisions.
Apply appropriate statistical techniques to data exploration and model development/assessment
Communicate complex statistical concepts to non-technical audiences; maintain skills through continuing education.
Create meaningful data visualizations to communicate results and highlight business impact
Effectively collaborate with others to deliver efficient and high-quality code (Python or R)
Implement solid validation process to ensure consistency and minimize errors in code
Champion more efficient ways to produce code, maintain skills through continuing education; stay up to date on new technologies/ tools
Seamlessly transition between languages to apply the right approach to the problem.
Understanding of Big Data infrastructure – Hadoop, MapReduce and Spark