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Accenture

Advises businesses and runs their technology

Junior Data Scientist - Pleno e Sênior

Data SciencePosted on Nov 12
Full-timeRemote in BrazilNo salary listedRemote

About the Job

Requirements

  • The junior-level candidate should know Python for data analysis and manipulation.
  • The junior-level candidate should know SQL for querying and handling data.
  • The junior-level candidate should be familiar with machine learning fundamentals and applied statistics.
  • The junior-level candidate should know libraries such as Pandas and scikit-learn.
  • The junior-level candidate should be familiar with data preparation, cleaning, transformation, and exploratory analysis.
  • The junior-level candidate should have basic knowledge of ETL/ELT.
  • The pleno-level candidate should have mastery of Python for data analysis and model development.
  • The pleno-level candidate should have solid SQL experience for data manipulation and analysis.
  • The pleno-level candidate should have practical machine learning experience and knowledge of deep learning and NLP.
  • The pleno-level candidate should have experience with libraries and frameworks such as Pandas, scikit-learn, TensorFlow, and/or PyTorch.
  • The pleno-level candidate should know applied statistics, including hypothesis testing, inference, and experimentation.
  • The pleno-level candidate should have experience with large data volumes and building ETL/ELT pipelines.
  • The pleno-level candidate should have experience with cloud environments using AWS, Azure, or GCP.
  • The senior-level candidate should have advanced mastery of Python and SQL and the ability to develop more complex analytical solutions.
  • The senior-level candidate should have solid experience with machine learning, deep learning, and NLP in production environments.
  • The senior-level candidate should have experience designing, validating, deploying, scaling, and monitoring models.
  • The senior-level candidate should have advanced knowledge of applied statistics, experimentation, and model evaluation.
  • The senior-level candidate should have experience with large data volumes and optimizing pipelines and processing.
  • The senior-level candidate should have experience with AWS, Azure, or GCP and integrating analytical solutions with corporate applications.
  • The senior-level candidate should be able to define technical approaches, solve complex problems, and provide technical support to less experienced professionals.
  • The candidate should have an analytical profile and be able to turn data into insights and solutions applicable to business challenges.
  • The candidate should be interested in solving complex problems through data, statistics, and artificial intelligence.
  • The candidate should be able to work collaboratively with multidisciplinary teams.
  • The candidate should be committed to quality, experimentation, and continuous improvement of solutions.
  • The candidate should demonstrate autonomy appropriate to their career level and interest in sharing knowledge with the team.

Responsibilities

  • Perform advanced data analyses to generate relevant business insights.
  • Develop machine learning, deep learning, and NLP models for different problems and needs.
  • Build and maintain ETL/ELT data pipelines, aiming for quality, efficiency, and scalability.
  • Develop, validate, deploy, and monitor predictive models in production environments.
  • Work with large data volumes, seeking processing and performance efficiency.
  • Integrate analytical solutions with corporate systems and applications, and automate processes related to data and models.
  • Monitor models using metrics such as accuracy, drift, and performance, in collaboration with engineering, product, and business teams.

Desired Qualifications

  • Knowledge of generative AI, including large language models, embeddings, and related applications.
  • Knowledge of MLOps, including continuous integration/continuous deployment, versioning, and model governance.
  • Experience deploying and scaling models in production environments.
  • Experience with monitoring, observability, and identifying model or data drift.
  • Experience optimizing pipeline, data-processing, and model performance.

About the Company

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Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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