Arcadis Recruitment Drive for 2024 Freshers

About Company : Arcadis is the world’s leading company delivering sustainable design, engineering, and consultancy solutions for natural and built assets. We are more than 36,000 people, in over 70 countries, dedicated to improving quality of life. Everyone has an important role to play. With the power of many curious minds, together we can solve the world’s most complex challenges and deliver more impact together.

Company Full Name : Arcadis
Job Role : Graduate Data Analyst
Degree Needed : BE/BTech/MCA/ME/BSc/BCA
Batch : Any Batch
Location : Bangalore

Job Description :
Roles and Responsibility :

Proficient in using Microsoft suite of technologies, such as Power BI, Power Apps, Power Automate, to solve complex business problems.
Manipulate, transform, and analyse data sets that are large and complex.
Build mathematical models in Python or R
Proficient in RDBMS – SQL
Plan, gather, analyse and document user and business information.
Incorporate, integrate, and interface technical knowledge with business / systems requirements.
Collect and organize data, data warehouse reports, spreadsheets and databases for analytical reporting.
Data collection, data input, monitoring, maintaining and reporting.

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Required Qualification and Skills :

To be eligible for this position, you should meet the following requirements:

Bachelors/Masters in Math, Statistics, Comp Science, Engineering, or another technical field.

Experience required: 0 – 2 years
Possess strong quantitative and qualitative research and analytical skills
Ability to communicate analysis with clarity and precision. An ideal candidate would have a strong grasp of both technical and business perspectives.

Ability to adapt quickly in handling multiple/evolving tasks as a result of new engagements and/or reprioritized deadlines
Hands-on experience in developing dashboards using BI tools.
Proficiency in analysing data using R and/or Python.
Good understanding of statistical analysis and hypothesis testing.
Experience in basic statistical methodology: e.g. clustering and testing, predictive modelling (e.g. decision trees, regressions)