Data Scientist
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 environments
Technical 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 plus
Business & 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 stakeholders
Personal 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