Data Analyst

Responsibilities:

Deep-Dive Exploration: Conducting thorough exploratory data analysis to identify underlying patterns, anomalies, and relationships within complex data sets.

Hypothesis Generation: Developing hypotheses based on initial data exploration to explain business outcomes.

Scientific Validation: Designing experiments to collect additional data and validate these hypotheses when required.

Insight Communication: Preparing and presenting comprehensive EDA reports to stakeholders, highlighting significant findings and strategic recommendations.

Skills Required:

Analytical Mindset: Exceptional ability to organize and interpret large sets of data to discern patterns and derive meaningful insights relevant to the business.

Business Intelligence Synthesis: Ability to gather insights into clear, "CXO-ready" summaries that highlight strategic recommendations rather than just technical metrics.

SQL Mastery: Intermediate to advanced skills in SQL are necessary for effective data extraction and exploration. Exceptional ability to write complex, performant queries to extract data varied sources and data environments.

Statistical Proficiency: Intermediate to advanced proficiency in statistics and statistical tools to summarize key findings accurately.

Analytical Engineering (Python/Polars): High proficiency in using Python (specifically Polars or Pandas) for data manipulation. You prefer code-based solutions over manual spreadsheets for scalability and reproducibility.

Modern Data Stack Familiarity: Experience working with (or a strong desire to learn) Apache Iceberg, DuckDB, and PySpark for managing large-scale data analysis.

Linux Familiarity: Comfortable working in a Linux environment via terminal/SSH to manage Python environments and background services.

Nice to Have:

Statistical Modelling: Familiarity with time series analysis, cohort analysis, cluster analysis, or simulations.

Ad-Tech Knowledge: Familiarity with the structure of Amazon Advertising reports and keyword match-type competition.

Automation: Experience with basic CI/CD or automation scripts to keep data pipelines and apps updated without manual intervention.

Machine Learning: Experience in building and implementing ML models or Decision Support Systems.

Advanced Analytics: Exposure to text analytics, NLP, or LLM/RAG application development.

What we Offer:

• Competitive salary
• Medical Benefits/Accident Cover
• Flexi Office Working Hours
• Fast paced start up