(Senior) Data Scientist - Freelance

Posted 17 days agoViewed
36 - 45 EUR per hour
Europe (ALBHRVCYPCZEDNKESTFINGRCISLLTULVAMLTDEUNORPOLROUSRBSVKSVNSWEHUN)Asia (AZEGEOISRJPNKAZ)Full-TimeData Science, Marketing
Company:Netguru
Location:Europe (ALB, HRV, CYP, CZE, DNK, EST, FIN, GRC, ISL, LTU, LVA, MLT, DEU, NOR, POL, ROU, SRB, SVK, SVN, SWE, HUN), Asia (AZE, GEO, ISR, JPN, KAZ), CET
Languages:English
Seniority level:Senior, 5+ years
Experience:5+ years
Skills:
PythonSQLData AnalysisData scienceA/B testing
Requirements:
5+ years of experience in data science, analytics, consulting, or related quantitative field. Skills in SQL queries, predictive modeling with Python or R. Proficiency in experimentation techniques, including causal inference and A/B testing. Experience in experiment design and statistical analysis. Ability to define success metrics and implement measurement strategies. Experience within the Marketing domain (especially with attribution and MMM modeling). Creative and critical mindset, thriving in fast-paced, dynamic environments to resolve ambiguous problems using a structured, hypothesis-driven, and data-supported approach. Strong interpersonal skills, ability to work cross-functionally and influence audiences. Ability to lead strategic projects to completion with cross-functional teams. Strong data analysis skills to work with large datasets. Good command of written and spoken English (C1).
Responsibilities:
Ensure development of MMM from third-party vendors, data correctness, and appropriate usage. Ensure robust model development and validation. Draw an experimentation plan and manage measurement. Use statistical techniques, hypothesis testing, and experimentation to validate findings. Conduct quantitative analysis and present data to uncover business drivers and insights. Build analytics experiments, reports, and dashboards; perform ad-hoc data analyses using SQL, R, or Python. Identify and measure levers to enhance metrics; make actionable recommendations. Offer clarity in ambiguous environments and collaborate across multiple teams. Ensure flow of learnings between the "external" and "internal" model.
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