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