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Data Scientist

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Summary

Fully remote Data Scientist (Poland) working with a pharma client via emagine: partnering with business stakeholders to define KPIs, run exploratory analysis, and build statistical, predictive, and ML models that guide brand investment decisions. Core stack: Python, statistics/ML, and data visualization; Azure/Databricks/Spark is a plus.

  • Industry: pharma

  • Location: fully remote

  • Rate: we’re open to your suggestions

  • Long-term cooperation

Responsibilities:

  • Collaborate with business owners to understand analytical needs and translate them into data science solutions.

  • Participate in the discovery and scoping phase of the initiative.

  • Define KPIs, performance measurement frameworks, and analytical methodologies.

  • Conduct exploratory data analysis and identify key business drivers.

  • Develop statistical, predictive, and machine learning models.

  • Create recommendations and insights supporting brand investment and resource allocation decisions.

  • Present findings and recommendations to business stakeholders.

  • Support implementation and operationalization of agreed analytical solutions.

  • Work closely with data engineers and AI engineers throughout the project lifecycle.

Key requirements:

  • 4+ years of experience as a Data Scientist.

  • Strong knowledge of statistics, machine learning, and predictive analytics.

  • Advanced Python skills.

  • Experience with data visualization and storytelling.

  • Ability to convert complex analytical findings into business recommendations.

  • Experience working directly with business stakeholders.

  • Strong problem-solving and communication skills.

  • Fluent English.

Nice to have:

  • Experience in marketing analytics, commercial analytics, or brand performance measurement.

  • Knowledge of optimization models and resource allocation methodologies.

  • Experience with Azure, Databricks, Spark, or large-scale data environments.

  • Familiarity with causal inference, forecasting, and advanced statistical modeling.

Skills

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See also

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