Core Competencies
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1-3 years of experience in Data Science, Analytics, or a related quantitative role
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Strong ability to translate business problems into structured data problems and analytical solutions
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Experience working with cross-functional stakeholders (e.g., Marketing, Product, Operations) to define KPIs, hypotheses, and success metrics
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Demonstrated ability to take models from experimentation to production environments
Technical Skills
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Proficiency in Python
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Solid understanding of machine learning fundamentals (supervised and unsupervised learning, model evaluation, feature engineering)
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Experience building and validating models such as:
Customer segmentation (clustering, behavioral profiling)
Churn prediction and retention modeling
Propensity modeling and marketing attribution
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Experience with SQL and working with structured and semi-structured data
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Familiarity with data visualization and BI tools (e.g., Power BI, Tableau, Looker)
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Ability to generate actionable business insights from data, not just models
Production & Engineering Mindset
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Experience deploying models into production (APIs, batch pipelines, or integration with applications)
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Understanding of MLOps fundamentals (model versioning, monitoring, retraining workflows)
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Experience with Git and collaborative development workflows
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Experience working with cloud environments (AWS, Azure, GCP) is a plus
Business & Analytical Thinking
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Strong problem-structuring skills and hypothesis-driven thinking
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Ability to design experiments (A/B testing) and measure marketing impact
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Understanding of marketing analytics concepts (CAC, LTV, funnels, cohorts, retention curves)
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Ability to clearly communicate findings to both technical and non-technical stakeholders
Personal Attributes
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Strong ownership mindset and ability to work independently
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Curiosity and proactive learning attitude
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Detail-oriented with high standards for data quality and analytical rigor
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Strong written and verbal communication skills