Vice President - Data Scientist

Job Description

A Position Overview

Position Title

Data Science

Department

TDABSG

Level/ Band

VP

Role Summary : He or She will be responsible for advancing analytics opportunities in the BFSIsector, managing project delivery, people, and stakeholders. The ideal candidate will have a strong background in advanced analytics and experience in leading high-impact analytics solutions and programs with Indian clients. He/she should have setup and managed large, scaled Business intelligence unit

B Organizational Relationships

Reports to

SVP

Supervises

NA

C Job Dimensions

Geographic Area Covered

Pan India

Stakeholders Internal

TDA-BSG and all other TATA AIA internal departments

External

Partners

D Key Result Areas

Stakeholder Management

Manage stakeholder interactions through regular updates and grow stakeholder engagement with channel partners.

Design, plan, and scope projects with stakeholders.

Explain project methodology and project approach to required stakeholders.

Project Management and Training

Manage end-to-end project deliverables, adhering to timelines, project budgets, and stakeholder expectations.

Ensure adherence to Standard Operating Procedures and maintain updated relevant documents.

Work with junior team members on projects and coach them on-the-job.

Conduct in-house training based on team requirements.

Delivery

Strong experience in multi-scale, multi- geography experience of setting up and managing a team of 20+ data scientists.

Manage the entire delivery process and be responsible for all aspects of a project to ensure high-quality standards.

Understand business problems and address them to solve client problems.

G Skills Required

Technical

Data processing and data science libraries of Python (NumPy, Pandas, Scikit learn, etc.).

Experience and understanding of working with massive datasets locally/ distributed using tools such as Apache Spark, and familiarity with packages like Vaex, and Dask, and using these datasets to train and develop and train machine learning algorithms.

Knowledge of analytical models such as promotion optimization, Natural Language Processing (NLP) including experience in (BERT and DisilBert Models), Cluster Analysis, Segmentation, Next best recommendation, Neural network models, Logistic Regression (Fraud Models, Lapsation etc), ANN based model, LSTM, Transformers, Attention Models, Bagging and boosting, Generative model,

Experience with cloud-based analytics platforms, such as Azure, GCP, or AWS.

Familiarity with automated training, deployment, and monitoring of models in production using ML-Ops pipelines.

Experience working on projects end-to-end, not just POCs.

Experience in machine learning and deep learning.

Deep expertise in Python, SAS.

Behavioral

Essential

Desired

Interpersonal skills

Communication skills

Creative thinking skills

Supervising/Leadership skills

Teamwork Skills

Influencing skills