Key Responsibilities
Business Problem Formulation & Solution Design
- Translate business requirements into well-defined data science problems
- Design analytical approaches aligned with business objectives and constraints
- Apply design thinking to develop structured and scalable data-driven solutions
Statistical Validation & OKR Alignment
- Validate business objectives and key results (OKRs) using statistical methods
- Ensure analytical outputs are aligned with measurable business goals
- Apply hypothesis-driven thinking for decision-making and evaluation
Data Preparation & Wrangling
- Perform data cleaning, transformation, and preprocessing using Python or R
- Handle structured and unstructured datasets for analysis and modeling
- Ensure high-quality datasets for downstream analytics and ML workflows
Insight Generation & Data Storytelling
- Conduct exploratory data analysis to identify trends and patterns
- Generate actionable insights through visualization and narrative storytelling
- Present findings using tools like Tableau or Power BI
Advanced Analytics & Machine Learning
- Develop and evaluate machine learning models for business use cases
- Perform iterative model tuning while balancing performance and resource efficiency
- Make informed decisions on model iteration cycles and stopping criteria
Automation & Analytical Efficiency
- Use Excel VBA for automation of repetitive analytical tasks and reporting
- Streamline workflows for faster data processing and reporting
Communication & Stakeholder Management
- Communicate insights clearly through written and verbal presentations
- Translate complex analytical findings into business-friendly narratives
- Collaborate with cross-functional teams to quickly understand new domains
Domain Adaptability & Business Acumen
- Rapidly acquire domain knowledge across industries
- Apply strong data literacy to diverse business contexts
- Use problem-solving and analytical reasoning to support decision-making
