dLocal

Data Science Team Leader

dLocal

full-time

Posted on:

Origin:  • 🇦🇷 Argentina

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Job Level

Senior

Tech Stack

AWSDockerPandasPySparkPythonPyTorchScikit-LearnSQLTensorflow

About the role

  • Lead a data science team responsible for models, analyses, and data products.
  • Responsible for the week-to-week project planning of your team and keeping each member accountable for their deliverables.
  • Act as a technical lead and people manager, dedicating time to mentoring and managing team members.
  • Drive the analytical and modeling direction in projects and ensure they meet accuracy, scalability, and business impact requirements.
  • Hands-on work in data analysis, modeling, and code; act as senior authority in data science methodologies, statistical analysis, and machine learning implementation.
  • Responsible for hiring for your team.
  • Communicate insights, project status, and planning between other teams and business stakeholders.
  • Help develop team members' careers within the company and deliver production-grade machine learning models and data solutions for large customers.

Requirements

  • Proven hands-on experience as a Data Scientist, with a track record of delivering impactful projects.
  • Profound insight into Python for data analysis and machine learning, and familiarity with core libraries (e.g., pandas, scikit-learn, TensorFlow/PyTorch, PySpark, etc.).
  • Wide experience building and deploying scalable machine learning models into production environments.
  • Experience leading remote data science or analytics teams in the past
  • Excellent written and verbal communication, especially in translating complex technical findings to non-technical stakeholders.
  • Enjoy writing documentation (e.g., for models, data sources, and experimental results) and understand why it's valuable.
  • A self-starter - You can identify business opportunities, formulate a data-driven approach, and implement it yourself.
  • Design and implementation of the overall architecture of your team’s data products and modeling pipelines.
  • Ensuring the entire analytics stack is designed and built for speed, scalability, and reproducibility.
  • Strong understanding of statistical analysis, machine learning algorithms, and experimental design (e.g., A/B testing).
  • Experience with deploying models as services (e.g., using APIs, containers like Docker).
  • Expert knowledge of SQL and Relational Databases.
  • Experience with model validation, data quality assurance, and MLOps principles.
  • Proficient understanding of code versioning tools, such as Git.
  • Written and spoken English.
  • Be a team player.
  • AWS knowledge is a strong plus.