Lead - Data Scientist (NLP / AI Engineer)

What you will do :

- Own high-impact data science problems : Translate ambiguous business and product questions into measurable hypotheses, analyses, experiments and deployable solutions.

- Diagnose business performance : Investigate drops, spikes and structural changes in conversions, funnels and key business metrics using statistical analysis, segmentation, cohort analysis and causal reasoning.

- Build AI / NLP / LLM systems : Design and productionize solutions using modern language models, embeddings, retrieval, classification, ranking, information extraction and generative AI techniques.

- Work with stakeholders : Partner directly with founders, product leaders and business stakeholders; present findings clearly, challenge assumptions with data and explain complex statistical results in simple language.

- Shape product decisions : Bring a strong product mindset to experimentation, metric design, user behaviour analysis and prioritization of AI capabilities.

- Engineer for production : Write clean, testable, maintainable code; build reliable data and ML pipelines; care about latency, scalability, observability and model quality in real-world systems.

- Raise the bar for the team : Establish analytical standards, mentor team members, review approaches and help define best practices across statistics, experimentation, ML and AI engineering.

What we are looking for :

- Strong foundation in statistics : Hypothesis testing, confidence intervals, regression, probability, experiment design, A/B testing, sampling, bias, variance and statistical significance.

- Strong data science fundamentals : Feature engineering, supervised / unsupervised learning, model evaluation, time-series or forecasting knowledge, and practical experience working with messy business data.

- Hands-on NLP / LLM experience : Experience with transformers, embeddings, RAG, prompt design, evaluation, fine-tuning or adaptation techniques, and production use of modern LLM APIs or open-source models.

- Engineering mindset : Strong Python and SQL skills, good software engineering practices, APIs, version control, testing and experience taking models or AI workflows into production.

- Business and product thinking : Ability to connect analysis and modelling decisions to user behaviour, product metrics, revenue and operational outcomes.

- Communication : Strong written and verbal communication. You should be comfortable presenting to senior stakeholders and explaining why a metric moved, how confident we are, and what action should follow.

- Ownership : Comfort operating in a fast-moving startup environment with high autonomy, incomplete information and end-to-end accountability.

Preferred background :

We are particularly interested in candidates with a strong academic and problem-solving background from premier engineering or quantitative institutions such as IITs, BITS, NITs or comparable institutions. Exceptional candidates from other backgrounds with a strong track record are equally welcome.

Experience profile :

- Approximately 5+ years of relevant experience in data science, applied machine learning, NLP, AI engineering or closely related roles.

- Demonstrated experience owning business-facing analytical problems, not just building offline models.

- Experience working with product, engineering and business teams in a cross-functional environment.

- Prior startup, high-growth product, SaaS, conversational AI or customer-experience technology exposure is a plus.

Technical toolkit :

- Core : Python, SQL, pandas / PySpark or equivalent, statistics, experimentation, machine learning.

- AI / NLP : Transformers, LLMs, embeddings, vector search, RAG, prompt engineering, evaluation frameworks; experience with libraries such as PyTorch, Hugging Face, LangChain / LlamaIndex or equivalents is useful.

- Production : APIs, Git, testing, cloud platforms, data / ML pipelines, containers and modern deployment practices.