Lead Data Scientist

JOB DESCRIPTION

Experience : 8 - 12 Yrs

Location : Mumbai

Designation : Lead Data Scientist

KEY 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 andtheir 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 thesenior-most leadership.

EXPERIENCE AND QUALIFICATIONS

• 8+ years hands-on in AI/ML and Data Science, with models that reached production and changed a business metric; a record ofleading a team — hiring, setting standards and developing next-in-line leaders.
• 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).
• The executive presence to hold a commercial argument, not only a technical one; genuine comfort with ambiguity; and awarenessof data privacy, governance and responsible-AI obligations.
• A BS or MS in Computer Science, AI/ML, Data Science or a related field. We index on what you have shipped, not on further qualifications.