AVP / DVP - Data Science

Designation: Assistant Vice President Data Science

Mumbai

Salary upto 40 LPA

8+ years of experience in AI and Data

Science roles.

Assistant Vice President

Your journey with us begins here, where success is more than just a possibility-

it's the Certainty of Life.

Bachelor's or Master's degree in
computer science, AI/ML, or a related

field.
Location: Grade:

roles and responsibilities?

AI & Machine Learning
Design, develop, and implement machine learning models for predictive analytics, natural language
processing (NLP), computer vision, or recommendation systems.
Lead end-to-end AI project lifecycles, including validation, productionization, and optimization using
frameworks such as TensorFlow, PyTorch, and scikit-learn.
Stay updated with the latest advancements in Generative AI and foundation models, evaluating and
integrating them as needed.
Partner with cross-functional teams to identify AI use cases and deliver measurable business value.
Data Science & Analytics
Lead the end-to-end analytics lifecycle, from data exploration to actionable insights and product
development.
Collaborate with data engineers and analysts to build impactful solutions that drive business outcomes.
Leadership & Strategy
Mentor and guide junior team members, fostering their professional growth.
Contribute to the enterprise AI and cloud strategy, including roadmap planning and vision setting.
Cloud Architecture & Engineering
Architect and design cloud-native AI and data platforms on GCP (preferred), AWS, or Azure.
Optimize data pipelines and machine learning workflows using services like Vertex AI, SageMaker, or
Azure ML.
Ensure scalable, secure, and cost-efficient architectures with robust CI/CD pipelines in cloud
environments.

Bachelor's or Master's degree in computer science, AI/ML, or a related field.
Education

Confidential

Work Experience

8+ years of experience in AI and Data Science roles.

Required Skills

Proficiency in Python and SQL.

Strong expertise in machine learning frameworks like TensorFlow, PyTorch, Hugging Face, and scikit-
learn

Hands-on expertise with large language models (LLMs), vector databases, and retrieval-augmented
generation (RAG) techniques
In-depth knowledge of cloud platforms, preferably GCP, but also AWS or Azure.
Familiarity with data engineering tools such as BigQuery, Kafka, Snowflake.
Sound knowledge of data privacy, governance, and ethical AI practices.
Expertise in MLOps practices, including CI/CD pipelines, model monitoring, and A/B testing.

Competencies

Strategic Thinking
Stake-holder/Vendor Management
Team Management
Domain Knowledge
Problem Solving/Solution Provider
Risk management