Sr. Data Analyst - AI Adoption_L7M

Hiring for: One of India’s leading non-banking financial companies (NBFCs), focused on driving financial inclusion across rural and semi-urban markets.

Role: Sr. Data Analyst - AI Adoption_L7M

Positions: 1

Experience: 4 to 6 years

Location(s): Kurla, Mumbai

Type: On-site / Permanent

Salary: Up to INR 22 LPA

JOB DESCRIPTION

1. Job Purpose Statement

We are looking for a highly skilled Sr. Data Analyst to join our AI Adoption team. This role is pivotal in driving the strategic use of data within Mahindra Finance, by analyzing complex business problems and delivering actionable insights. Leveraging data and analytics, the Data Analyst plays a crucial role in enhancing our understanding of customer behavior and generating actionable insights across our digital assets such as Website, Mobile App, and various offline channels. This role requires deep hands-on expertise in Databricks, PySpark, Python, and SQL to support advanced analytics, campaign design, and business growth initiatives.

You will work closely with the Campaigns Execution team to generate insights, optimize customer targeting, and execute data-driven business campaigns aimed at increasing portfolio growth, customer engagement, and overall profitability.

2. Duties & Responsibilities

•

Data Engineering & Analysis

-

Develop, optimize, and maintain scalable data pipelines on Databricks.

-

Perform complex data transformation and analysis using PySpark, Python, and SQL.

-

Work with large-scale structured and unstructured datasets to extract meaningful insights.

-

Automate data workflows and improve data quality and reliability.

•

Campaign Analytics & Execution

-

Collaborate with business and marketing teams to understand campaign objectives and data requirements.

-

Design and execute customer segmentation models for campaigns such as cross-sell, upsell, EMI conversions, collections strategies, lead scoring, etc.

-

Analyse campaign performance, identify actionable insights, and recommend optimization strategies.

-

Support end-to-end campaign execution using analytics-driven targeting logic.

•

Business Insights & AI Adoption

-

Identify opportunities to drive business impact through AI/ML models and data-driven decisioning.

-

Translate insights into actionable strategies to improve customer engagement and business outcomes.

-

Work with the AI/ML team to operationalize models into campaigns and business processes.

-

Contribute to the roadmap for AI adoption across business units.

•

Collaboration & Stakeholder Management

-

Work closely with product, risk, marketing, and operations teams to implement campaign strategies.

-

Communicate findings and insights clearly to both technical and non-technical stakeholders.

-

Ensure adherence to data governance, security, and compliance standards relevant to NBFC operations.

3. Key Challenges

•

Understanding Complex Business Processes: Need to understand and map out complex business processes. This can be challenging, given the role demands working with multiple departments and functions.

•

Bridging the Gap between IT and Business: Act as a bridge between the IT department, business units, and analytics team. This can be challenging as it requires a deep understanding of both technical and business aspects.

•

Keeping Up with Technological Advances: The field of analytics is constantly evolving with new technologies and methodologies. Keeping up with these changes and learning new tools and techniques are important.

•

Stakeholder Management: Managing expectations and maintaining clear communication with various stakeholders can be difficult. Stakeholders may have different priorities, and it can be a challenge to balance these and keep everyone informed.

•

Data Quality and Management: Ensuring the quality and integrity of data used for analysis can be a challenge. Poor quality data can lead to inaccurate analysis and decision-making.

•

Change Management: Implementing new systems or processes can be met with resistance from employees. Managing this change and ensuring a smooth transition is important.

4. Decision Making Authority

•

Decisions made alone – Project management, generating insights, stakeholder management, and other daily business as usual tasks

•

All items requiring key architectural choices, project scope, investments, additional expense should be consulted with manager and above.

5. List of internal and external stakeholders

•

Internal stakeholders – Interaction with fellow members of the Data Science team, and various other Business, IT, and Technology team

•

External stakeholders – Interactions with the vendors and data science community globally & Mahindra group company leaders

6. Organisational Relationship

Lead AI Adoption

Sr. Data Analyst AI Adoption

Employee Name

7. Job Requirements

•

Professional Qualification

-

Master's in science, Stats, Business/ Bachelors in Engg, Tech

-

3-5 years of experience working in Data Analytics in Banking/Finance industry

-

Proven track record in delivering data analytics projects to clients/internal stakeholders.

•

Knowledge

-

Working knowledge of Data Science toolbox like: Python, PySpark, SQL, Jupyter Notebook, Azure/AWS cloud and/or experience with machine learning is a plus

-

Deep understanding of HDIEP Framework – Hypothesis, Data Sourcing, EDA, Insights and Presentation

•

Skills

-

High degree of Emotional Intelligence in managing relationships with internal clients /stakeholders.

-

Act as the translator between analytics, IT, and Business teams to effectively deliver business outcomes

•

Attitude

-

Result orientation with execution excellence

-

Customer Focus

-

Weaving passion and energy at work

-

Can Do and Will Do attitude