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 straightforward 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.

📌 Lead Data Scientist - NLP/LLM (India)
🏢 The Reliable Jobs
📍 India