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full-time·Psudo

Data Analyst - Data Scientist

United StatesCompetitive1 skills

Skills & Stack

SQL

About the Role

Join Pseudo as a Data Analyst to generate actionable insights for operational excellence and revenue optimization. You will be responsible for compiling insights from data and assisting in building data-driven processes. This role involves working with large datasets, optimizing data flows, and collaborating with program managers to drive high-impact initiatives.

About Psudo

Psudo is a company focused on sustainable production and retail, with a mission to drive operational excellence and revenue optimization through data-driven insights. The company is known for its commitment to fashion and sustainable practices.

What You'll Do

7 items
  • 1Ensure smooth data flow from source to destination for processing
  • 2Utilize strong database skills to work with large, complex datasets to extract insights
  • 3Filter and cleanse unstructured data into usable datasets for analysis
  • 4Identify new data sources to support analytics initiatives
  • 5Build tools for automating repetitive tasks to free up bandwidth for analytics
  • 6Collaborate with program managers and business analysts to generate actionable insights
  • 7Work closely with top management to prioritize information and analytic needs

What We're Looking For

  • Bachelor’s or Master’s degree in a quantitative field such as Engineering, Statistics, Math, Economics, or Computer Science with Modeling/Data Science
  • 2-3 years of work experience preferred, but freshers are welcome
  • Ability to program in high-level languages, familiarity with R and statistical packages preferred
  • Proven problem-solving and debugging skills
  • Experience with database technologies and tools (SQL, R, SAS, JMP), data warehousing, transformation, and processing
  • Experience with text analytics, data mining, and social media analytics
  • Knowledge of standard statistical techniques such as Logistic Regression, Classification models, Cluster Analysis, Neural Networks, Random Forests, Ensembles, etc.
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