Principal Data Scientist

Job Title: Principal Data Scientist

Company Info:

TVS Digital is a digital technology start-up focused on the Fintech & Automotive industry with platforms and services offerings in the Credit & Collection Services and Automotive & Retail domain. We provide IT Services to accelerate digital transformation by partnering with industry renowned platforms (Microsoft, Salesforce, Oracle, SAP) & help to develop centre of excellence for our customers. TVS Digital’s mission is to build & deliver high quality software solutions & services to help address real life business challenges of global companies in these domains by harnessing the power of Analytics, Artificial Intelligence (AI), Machine Learning (ML). Our strategy is to leverage inorganic and organic means for the initial build out and subsequent expansion and growth. TVS Digital is a part of TVS Holdings and has also currently invested in several early-stage high tech start-ups globally in areas of connected manufacturing, fleet management and credit services.

Location: Chennai

Experience: 8-12 Years

Work Mode: Work From Office

The ideal candidate will have a strong background in data analytics, statistics, programming, and data visualisation, as well as experience using data to generate actionable insights, drive business decisions, and productionize machine learning models. The candidate will be responsible for working with cross-functional teams to identify opportunities for leveraging data to solve business problems, developing and implementing analytical models, and productionizing models for deployment in a production environment.

Responsibilities:

• Work with cross-functional teams to identify opportunities for leveraging data to solve business problems
• Collect, clean, and transform data from various sources
• Analyse and interpret large datasets using statistical techniques and data visualisation tools
• Develop and maintain predictive models using Python
• Evaluate the performance of analytical models and make recommendations for improvements
• Productionize machine learning models for deployment in a production environment
• Develop and maintain code for model deployment, monitoring, and retraining
• Explain the model to both technical and non-technical stakeholders, including the model assumptions, limitations, and potential impact on the business
• Communicate insights and model explanations to both technical and non-technical stakeholders using compelling data visualisations and storytelling techniques
• Develop presentations and reports to share insights and model explanations with senior management and other stakeholders

Requirements:

• Master's or Ph.D. degree in Computer Science, Engineering, Statistics, Mathematics, or related field
• At least 8+ years of experience working as a data scientist or similar role
• Strong programming skills in Python
• Strong understanding of statistical analysis techniques and data visualization tools
• Experience working with large datasets, including data cleaning, transformation, and visualization
• Experience productionizing machine learning models for deployment in a production environment
• Excellent communication skills, with the ability to effectively communicate insights and model explanations to both technical and non-technical stakeholders
• Strong problem-solving skills and attention to detail
• Ability to work collaboratively in a team environment

Behavioral competency

• Owns deliverables end-to-end; takes responsibility for both success and failure.
• Drives cross-functional alignment, even when not directly managing stakeholders.
• Can clearly explain complex models and insights to non-technical stakeholders.
• Makes progress in ambiguous, noisy, or incomplete data environments.
• Believes in experimentation, feedback loops, and continuous improvement.
• Breaks down problems methodically rather than relying on templates.

Tech stack:

1. Databricks

2. Azure microservices - Azure functions for model deployment

3. Visual basic code ide

4. Jupyter notebook

5. Python

6. Sql & Pyspark for data processing

7. Github for model versioning and setting up CICD pipelines