Scientific Lead

Há 5 dias

São Paulo, São Paulo, Brasil beBeeData Tempo inteiro R$90.000 - R$120.000
Job Description

Artefact is seeking a skilled data professional to join our team as a Senior Data Scientist. As a key member of the data science team, you will be responsible for designing, building, and maintaining scalable data products that support our analytics and data-driven products.

Responsibilities include:

  • Designing, developing, and deploying end-to-end machine learning models, from data acquisition and feature engineering to production monitoring.
  • Collaborating with data analysts, product managers, and engineering teams to translate business problems into data science frameworks and ensure impactful outcomes.
  • Developing and refining complex data features from large-scale datasets to improve model accuracy and business performance.
  • Championing data quality and integrity, defining the requirements for robust datasets needed for advanced analytics and modeling.
  • Monitoring, analyzing, and troubleshooting the performance of deployed models, addressing issues like concept drift, data quality degradation, and performance bottlenecks.
  • Providing technical leadership in the application of statistical modeling, machine learning, and advanced analytics to solve complex business problems.
  • Documenting and presenting complex methodologies, model findings, and strategic recommendations to both technical and non-technical stakeholders.
  • Leading by example, championing a culture of scientific rigor, experimentation, and continuous learning within the data science team.

We are looking for someone who has strong communication and interpersonal skills, with the ability to collaborate across multiple teams and levels of the organization. You should have demonstrated ability to balance hands-on technical delivery with mentoring and team enablement.

A degree in Computer Science, Statistics, Engineering, Economics, or a related quantitative field is required. Experience with several tools and technologies in the modern data stack, including programming languages (Python), cloud data platforms (GCP, AWS, Azure, Databricks), data warehousing & databases (PostgreSQL, SQL Server, BigQuery, Snowflake), machine learning models (Supervised learning models, Unsupervised learning models), MLOPS (Databricks, mlflow, AWS Sagemaker, Vertex AI), and Git code versioning workflow and platforms is preferred.

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