Data Scientist for Business Intelligence

5 dni temu

Warszawa, Mazovia, Polska TN Poland Pełny etat

Company Overview

TN Poland is a leading provider of data analytics solutions. Our team is dedicated to delivering innovative technologies and insights to our clients.

Job Description

The Staff ML Scientist will work with a team to conduct world-class applied AI research on data analytics, contributing to the long-term research agenda in large-scale data analytics and machine learning. This role represents an exciting opportunity to make key contributions to our strategic vision as a world-leading data-driven company.

The successful candidate must have strong academic credentials and demonstrate excellent software engineering skills. They should be comfortable with ambiguity, possess a strong attention to detail, and exhibit excellent collaboration skills.

Required Skills and Qualifications

  • Formulate business problems as technical data problems while ensuring key business drivers are collected in collaboration with product stakeholders.
  • Work with product engineering to ensure implementability of solutions. Deliver prototypes and production code based on need.
  • Experiment with in-house and third-party data sets to test hypotheses on relevance and value of data to business problems.
  • Build needed data transformations on structured and unstructured data.
  • Build and experiment with modeling and scoring algorithms. This includes development of custom algorithms as well as use of packaged tools based on machine learning, analytics, and statistical techniques.
  • Devise and implement methods for adaptive learning with controls on efficiency, methods for explaining model decisions where vital, model validation, A/B testing of models.
  • Devise and implement methods for efficiently monitoring model efficiency and performance in production.
  • Devise and implement methods for automation of all parts of the predictive pipeline to minimize labor in development and production.
  • Contribute to development and adoption of shared predictive analytics infrastructure.

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