AI Engineer/ Data Scientist

Job Description:-

AI Engineer/Data science- Team Leader

The purpose of this position is to collect, process, and analyze data to help organizations make informed business decisions as well as to interpret complex data sets, identify trends, and generate actionable insights that support strategic goals, improve efficiency, and drive business growth.

Educational Qualification

Bachelor's degree in Data Science, Computer Science, Mathematics, Statistics, Economics, or a related field. A Masters degree or relevant certifications is a plus.

Work Experience Required (In yrs.) - 5+ Years Relevant

Technical Skills

Tableau

Python

SQL/Oracle

Advanced Excel

Data Management

Data Visualization

Advance Data Analysis & Statistics

Other Skills

Leadership and Team Management

Report making skills

Critical Thinking

Presentation skills

Analytical Thinking

Key Areas of Responsibilities

1. Lead, mentor, and develop a team of data analysts, fostering a culture of collaboration, growth, and continuous improvement.

2. Conduct regular performance reviews, set clear goals, and provide constructive feedback to enhance individual and team performance.

3. Gather, clean, and structure large datasets from multiple sources for analysis.

4. Perform statistical analysis and interpret data to identify trends, patterns, and insights.

5 )Develop and maintain reports, dashboards, and data visualizations to support business decisions.

6. Collaborate with cross-functional teams to understand business needs and translate them into data analysis tasks.

7. Use SQL, Oracle, Python, Tableau or other tools to query, process, and analyze structured and unstructured data.

8. Ensure data accuracy, consistency, and integrity through regular validation and quality checks.

9. Automate and streamline data analysis processes to improve efficiency and accuracy.

10. Capable of performing analyses related to Sales performance, Manufacturing processes, and procurement activities & customer.

11 )Consolidate various MIS reports into a single comprehensive report.

12 )Stay updated on industry trends and best practices in data analytics and tools.

13. Familiarity with data warehousing, ETL processes, and large-scale data management.