Data Scientist

We are looking for a Full Stack Data Science and Analytics professional to join our team. This is a hybrid role designed to bridge the gap between deep statistical modeling and agile product growth. The ideal candidate will handle the end-to-end data lifecycle: from maintaining robust data ingestion/quality to building sophisticated predictive models and leading product experimentation (A/B testing) to drive user engagement and business efficiency.

• Data Science & Modeling
• Predictive Modeling:
  Create predictive models, statistical reporting, and data analysis methodologies to identify trends in large, complex datasets.
• Cross-Functional Application:
  Apply analysis to various areas of the business, including but not limited to Market Economics, Supply Chain, Marketing/Advertising, and Scientific Research.
• Forecasting:
  Use predictive and prescriptive analytics tools to forecast business outcomes using probabilities and defined confidence levels.
• Innovation:
  Maintain up-to-date knowledge of existing and emerging scientific principles, theories, and techniques to identify and develop innovative solutions and projects.
• GenAI Integration:
  Leverage the latest developments in Generative AI technologies to improve efficiency in company business processes and automate manual workflows.
• Product Analytics & Experimentation
• Experimentation Lifecycle:
  Design, execute, and analyze A/B tests and multivariate experiments (MVT). This includes hypothesis generation, sample size calculation, and determining statistical significance.
• User Behavior Insights:
  Utilize Product Analytics Tools such as Google Analytics (GA4) and Looker to map user journeys, identify drop-off points, and recommend features that increase product "stickiness."
• Strategy & Storytelling:
  Translate complex statistical findings into actionable insights for high-level stakeholders, including senior leadership and the CEO.

• Education:
  
  Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Economics, or a related quantitative field.
• Professional
  
  Experience:
  
  Minimum of 5 years in Data Science, Product Analytics, or a closely related quantitative field.
• Product Mindset: A strong "product-first" lens—the ability to ask why users behave a certain way, not just what the data says.
• Technical Capabilities:
  Advanced SQL (CTEs, Window Functions) and Python (Pandas, Scikit-learn, Stats models) is a must.
• Expert-level proficiency in Product Analytics Tools (Google Analytics (GA4) and Looker).
• Deep understanding of A/B Testing and experimental design (Frequentist or Bayesian) is required.
• Advanced Excel (Financial modeling, complex formulas, and data manipulation) is a must.
• Experience with time-series analysis (e.g., Prophet, ARIMA) and growth modeling (S-Curves) is a must.
• Communication:
  Exceptional ability to simplify complex technical concepts for non-technical executive audiences.
• Adaptability:
  Comfort moving between long-term research projects and fast-paced experimentation cycles.

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