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

- Solid 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

📌 IN_Manager_Lead Data Scientist_Enterprise APPS SFDC_Advisory_Mumbai (Mumbai)
🏢 PwC India
📍 Mumbai