Senior Data Analyst
Bengaluru, India / Gurugram, India
Data Science – Data Science /
Full Time /
Hybrid
Who is AiDash?
AiDash is making critical infrastructure industries climate-resilient and sustainable with satellites and AI. Using our full-stack SaaS solutions, customers in electric, gas, and water utilities, transportation, and construction are transforming asset inspection and maintenance - and complying with biodiversity net gain mandates and carbon capture goals. Our customers deliver ROI in their first year of deployment with reduced costs, improved reliability, and achieved sustainability goals. Learn more at
What will you do?
- You will work with some of Aidash’s most fundamental and existing data and use that data to help drive company wide initiatives.
- You will build, deploy & monitor key business metrics , dashboards and reports to inform.
- You will built statistical techniques to access the data quality issues.
- You will deliver actionable business recommendations through analysis and data story telling.
- Analyse internal and external data sources and identify new data analysis opportunities
- Identify, analyse, and interpret trends or patterns in complex data sets.
- You will work on key business problems to Identify and resolve through data driven solutions.
What are we looking for?
- Bachelors or Master's in Computer Science,Mathematics, Statistics, or a related field.
- At least 2-4 years of relevant experience as a data analysis, building data pipelines and analysing large datasets to solve problems.
- Proficiency in SQL and Python.
- Strong statistical knowledge.
- Proven ability to manage and deliver on multiple projects with great attention to details.
- Experience in collaborating across functions to identify data analytics problems and execute solutions with technical rigor and data driven insights.
- Can straddle the line between analytics and data science
- Dives deeply into larger more ambiguous data questions
What other experience will make you a great candidate?
- Familiarity of machine learning and deep learning.
- Familiarity of distributed processing systems i.e., spark or Hadoop.
