Sr. Data Scientist

Keka Inc.; Ahmedabad District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Keka Inc. is seeking a data science leader in Ahmedabad to coordinate a junior team and drive end-to-end projects from business framing to deployment. The role requires deep expertise in Python, SQL, ML, statistics, and GenAI, with strong leadership and communication skills to align technical solutions with business needs.

The candidate will monitor execution, validate performance against requirements, ensure best practices, and deliver clear, QA’d documentation and results to stakeholders.

Qualifications

• Framing and translating business problems into technical solutions.
• Strong command of Python, SQL, ML and statistics.
• Ability to translate complex analyses into actionable insights for stakeholders.
• Experience guiding data science projects from inception to deployment.
• Familiarity with data science best practices and governance.
• Ability to document methods, results and technical decisions clearly.
• Proven leadership and people management capabilities.

Responsibilities

• Coordinating the junior Data Science team throughout the entire engagement.
• Leading the business framing phase and translating it into the technical solution.
• Providing expertise to identify the optimal solution/algorithm for each use case.
• Continuously monitoring implementation activities to highlight issues or potential delays.
• Validating implemented solutions in terms of performance and requirements.
• Ensuring compliance with data science best practices.
• Quality assurance of the solution and related technical documentation.
• Presenting results to business stakeholders.

Skills

Python

SQL

ML

Statistics

GenAI

Cloud/MLOps

Domain expertise

Business communication

Leadership

Job description

1.Coordinating the junior Data Science team throughout the entire engagement

2.Leading the business framing phase and its translation into the technical solution

3.Providing expertise into finding the optimal solution/algorithm for each use case.

4.Continuously monitoring the implementation activities to promptly highlight any technical issue or source of potential delay

5.Validating the implemented solution in terms of technical performances and adherence with the functional requirements previously collected

6.Ensuring compliance of the implemented solution to data science best practices

7.Quality assurance of the solution and the related technical documentation

8.Results presentation to business stakeholders

Python + SQL + ML + Statistics + GenAI + Cloud/MLOps + Domain expertise + Business communication + Leadership