IN_Manager_Lead Data Scientist_Enterprise APPS SFDC_Advisory_Mumbai

PwC India; Mumbai; On-site; INR 4,500,000 - 6,000,000; Full time

Job summary

PwC India is seeking a senior ML/AI leader to conceptualise business problems and translate them into scalable analytical solutions. You will transform prototypes into production-ready AI/ML applications, leveraging NLP, CV and generative models across complex datasets.

The role requires depth in ML algorithms, feature engineering, and MLOps with cloud services, plus ability to guide cross-functional teams and senior leadership. 8+ years of experience expected.

Qualifications

• Strong command of Python and SQL across large datasets with robust, testable code.
• Depth in feature engineering, statistics and ML algorithms (regression, classification, clustering, neural networks, time-series), and in Generative AI, NLP and Computer Vision.
• Hands-on with language models for retrieval — embeddings, RAG, vector stores, prompt design and evaluation.
• MLOps and engineering discipline to ship: experiment tracking, model registry, versioning, containerisation, CI/CD and cloud AI services.

Responsibilities

• Conceptualise business problems, drive frameworks, and translate ambiguous asks into solvable analytical problems.
• Transform data science prototypes into production-grade solutions; design AI/ML applications against defined business and technical requirements.
• Leverage large language and vision-language models for retrieval, extraction and reasoning over unstructured enterprise data.
• Find and implement the right algorithms and tools, balancing accuracy, latency, interpretability and cost; train, evaluate and refine.
• Integrate models into the application flow and deploy at scale, with monitoring for drift, degradation and failure.
• Set up the infrastructure for data analysis and mining required to generate actionable insight reliably.
• Use effective feature engineering and pre-processing across structured and unstructured data; select or define annotated datasets and their quality controls.
• Extend ML libraries and frameworks so they apply across a range of tasks.
• Establish responsible-AI practice — model documentation, bias and privacy review, PII minimisation and audit trails.
• Institutionalise measurement — A/B tests, holdouts and causal inference — so every model carries a defensible business number.
• Create dashboards and visualisations that present data in a logical, decision-ready way to stakeholders.
• Set up and lead your own team, drive the vertical, and develop next-in-line leaders.
• Collaborate with cross-functional teams of diverse backgrounds; communicate insight coherently, working directly with the senior-most leadership.

Skills

Python

SQL

ML algorithms

Generative AI

NLP

Computer Vision

MLOps

Cloud AI services

RAG/embeddings

Education

BS/MS in Computer Science

Tools

CI/CD

Containerisation

Job description

Responsibilities

Models and machine learning

• Conceptualise business problems, drive frameworks, and translate ambiguous asks into solvable analytical problems.
• Transform data science prototypes into production-grade solutions; design AI/ML applications against defined business and technical requirements.
• Leverage large language and vision-language models for retrieval, extraction and reasoning over unstructured enterprise data.
• Find and implement the right algorithms and tools, balancing accuracy, latency, interpretability and cost; train, evaluate and refine.
• Integrate models into the application flow and deploy at scale, with monitoring for drift, degradation and failure.

Data foundation and infrastructure

• Set up the infrastructure for data analysis and mining required to generate actionable insight reliably.
• Use effective feature engineering and pre-processing across structured and unstructured data; select or define annotated datasets and their quality controls.
• Extend ML libraries and frameworks so they apply across a range of tasks.

Responsible AI, measurement and governance

• Establish responsible-AI practice — model documentation, bias and privacy review, PII minimisation and audit trails.
• Institutionalise measurement — A/B tests, holdouts and causal inference — so every model carries a defensible business number.
• Create dashboards and visualisations that present data in a logical, decision-ready way to stakeholders.

Team and stakeholders

• Set up and lead your own team, drive the vertical, and develop next-in-line leaders.
• Collaborate with cross-functional teams of diverse backgrounds; communicate insight coherently, working directly with the senior-most leadership.

Mandatory skill sets

• Strong command of Python and SQL across large datasets, with robust, testable code and sound software architecture.
• Depth in feature engineering, statistics and ML algorithms (regression, classification, clustering, neural networks,time-series), and in Generative AI, NLP and Computer Vision and their business applications.
• Hands-on with language models for retrieval — embeddings, RAG, vector stores, prompt design and evaluation — plus
• MLOps and engineering discipline to ship: experiment tracking, model registry, versioning, containerisation, CI/CD and cloud AI services (AWS / GCP / Azure).

Preferred skill sets

• Experience in a high-ticket, considered-purchase category — real estate, automotive, BFSI or luxury retail — where the funnel is long and the sample small; geospatial and location analytics; and productionising generative AI in a regulated or PII-sensitive environment.

Years of experience required

8+ years

Education qualification

BS/MS in Computer Science