About this role

We are looking for a Lead Data Scientist comfortable framing an ambiguous business problem, someone with broad methodological range and the flexibility to choose the right approach for each problem.

The problems we work on cut across supervised and unsupervised learning, causal inference, optimization, recommendation, forecasting, and increasingly LLM-based systems including Agentic AI. Candidate should be able to use the right tool for the problem while keeping the approach driven by the business question and hence lead by example. This is a hands-on (70% IC work involving modeling, coding, system design and 30% mentorship and stakeholder engagement.

What youll do

- Partner with stakeholders to frame the problem and develop methodology across the full spectrum . Work directly with business leaders and domain experts to translate ambiguous questions into well-posed analytical problems, align on success metrics, communicate trade-offs clearly, and recommend the most effective approach: Supervised Learning, Unsupervised Learning, NLP, Generative / Agentic AI, Causal Machine Learning, Optimization, Recommendation Systems and Forecasting or rule-based decision based on impact, constraints, and maintainability

- Apply LLMs and agentic systems pragmatically, and drive adoption of evolving capabilities. Continuously evaluate new models, tooling and patterns; run fast, measurable prototypes; and scale the winners into production RAG/agent workflows i.e., owning evaluation, guardrails and hallucination control

- Build and own end-to-end production systems. delivering robust, observable services that run reliably at scale i.e., from data cleaning and feature engineering through model development, evaluation, deployment, monitoring, retraining and incident

- Set the technical bar and lead by example.



Define and uphold standards for experimental design, offline/online evaluation, A/B testing, causal validity, model governance and reproducibility then reinforce them through hands-on mentorship, pairing on hard problems and thoughtful code/design reviews that grow the team's craft and depth

What youll need

This role is designed for a hands-on Data Science leader who combines strong fundamentals with strong engineering instincts. You'll likely recognize yourself in many of the following:

- Strong judgment. You balance rigor, speed and maintainability and can explain trade-offs in a way that helps teams and stakeholders make good decisions

- Hands-on, by choice. You write production-quality Python code, review code thoughtfully and treat coding as a core part of how you build and think

- Systems builder. You've taken solutions from a blank repo to production systems that real users or business processes depend on

- Strong in fundamentals. You can explain why methods work, when they break and what assumptions they rely on in explicit, simple language. Linear algebra, probability, optimization and statistical inference are tools you use actively

- Comfortable with ambiguity. You can take a vague problem, clarify goals and constraints, and turn it into a well-defined plan before selecting the approach

Experience

- 8+ years in Applied ML / Applied AI (including LLM and GenAI),



with 6+ years hands-on experience building and deploying models in production environments

- Bachelors or Masters (or PhD) in Computer Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field. Equivalent demonstrable expertise is welcome. Strong working knowledge of classical ML and statistics: regularized regression, tree-based methods, gradient boosting, clustering, dimensionality reduction, hypothesis testing, and experimental design

- Modern LLM stack: RAG, agentic workflows, evaluation frameworks, prompt engineering, fine-tuning trade-offs, and vector databases

- Deep learning fundamentals: understanding when to apply deep learning approaches and how to train and evaluate them effectively in practice

- NLP fundamentals: text preprocessing, embeddings, similarity/search, topic modeling, classification, and evaluation

- Causal ML: experience with at least one: DML, uplift modeling, IV, propensity scoring, synthetic control, or difference-in-differences, applied in a decision-making or production context

- Optimization: linear/integer programming, constrained optimization, bandits, or RL applied to real-world problems

- Expert-level Python. Comfortable with the scientific stack (NumPy, pandas, scikit-learn, PyTorch) and with writing clean, tested, modular code

- Working knowledge of Agentic AI Frameworks like Langchain, Langgraph and Deepagents or equivalent

- Cloud Computing (AWS / Azure / GCP) - model training, deployment, scaling, cost-awareness

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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