Data Anlayst

**Position Required Skills, Knowledge, and Abilities**:

- Minimum 3 years of experience.
- Hands-on
experience using Microsoft Excel and formulae within.
- Knowledge
of different data types and visualizations.
- Understanding
of reporting structure and different types.
- Good
to have basic knowledge of Python and SQL.
- Good
in analyzing, logical reasoning, and understanding workflows.
- Strong
proficiency in MS Excel is a must.
- Strong
proficiency in programming languages such as Python or R for data
manipulation, analysis, and visualization.
- Solid
understanding of data analysis techniques, including statistical analysis,
machine learning, and data visualization.
- Experience
with AI/ML concepts and techniques, such as regression, classification,
clustering, and neural networks.
- Familiarity
with popular data analysis libraries and frameworks, such as pandas,
NumPy, sci-kit-learn, or TensorFlow.
- Proficiency
in SQL and experience with relational databases for data querying and
manipulation.
- Stay
up to date with the latest advancements in AI/ML technologies, data
analysis techniques, and industry best practices.- Document
methodologies, processes, and findings to ensure the reproducibility and
transparency of data analysis workflows.

**Position Education and Experience (required and**
**preferred)**:

- Bachelor
of Engineering, MCA, MSc. Computer Science is mandatory.

**Position** **Responsibilities**:

- Create
and run SQL queries to fetch data from DB.
- Write
scripts to automate data manipulation activities.
- Cleanse
the data and ensure data is good for reporting.
- Build
reports based on the client’s requirements and KPI.
- Present
reports to clients, get confirmation and publish the same.
- Analyze
and generate insights from the data.
- This
role requires a strong understanding of data analysis techniques,
experience with AI/ML concepts, and proficiency in programming and data
manipulation.
- Analyze
complex datasets using statistical techniques, machine learning
algorithms, and data visualization tools to derive insights and identify
patterns.
- Collect,
clean, and pre-process large volumes of data to ensure its suitability for
AI/ML model development.