Senior Manager - Data Science

noon; Gurugram District; On-site; INR 6,000,000 - 9,000,000; Full time

Job summary

noon is building an ecosystem of digital products and services powering everyday life across the Middle East. We are hiring a senior leader for Applied AI, analytics automation and internal tools to connect LLMs with governed data and deliver trusted insights at scale.

You will lead analytics automation, product analytics, and a data-driven culture, mentoring analysts and data scientists, while partnering with PMs and engineering to ship measurable impact on revenue and user experience.

Qualifications

• 7-9 years in product analytics, data science or growth analytics at scale.
• 2+ years managing analysts or data scientists including hiring and reviews.
• Strong SQL and Python, with a cloud warehouse (BigQuery preferred) and Looker.
• Production experience applying LLMs to analytics or internal tools and safeguards.

Responsibilities

• Lead analytics automation by connecting LLMs to governed data and semantic layers.
• Ensure AI-generated insights are accurate, explainable and secure with governance from the start.
• Build internal tools and self-serve products for teams to answer questions and reduce ad-hoc requests.
• Identify opportunities and translate them into adoptable solutions for teams.
• Shape customer discovery, engagement, and buying experiences with personalized recommendations.

Skills

SQL

Python

BigQuery

Looker

LLM integration

Experimentation design

Stakeholder communication

Product analytics

Tools

SQL

Python

BigQuery

Looker

Job description

We’re building an ecosystem of digital products and services that power everyday life across the Middle East—fast, scalable, and deeply customer-centric. Our mission is to deliver to every door every day. We want to redefine what technology can do in this region, who can help us move even faster.

noon’s mission: Every door, every day.

What you’ll do

Applied AI, analytics automation and internal tools (must have)

• Lead analytics automation by connecting LLMs to governed data: certified metrics, curated tables and semantic layers.
• Ensure AI-generated insights are accurate, explainable and secure, with clear evaluation, transparency and data governance built in from the start.
• Build internal tools and self-serve products that help teams answer their own questions and reduce ad-hoc requests.
• Spot opportunities nobody has asked for yet, and turn the best into solutions teams actually adopt.
• Build solutions that shape how customers discover, engage with, and buy on noon, from personalized recommendations to a smarter, more relevant app experience.
• Choose the simplest method that works, and measure models on business outcomes, not just offline accuracy.

Product analytics and experimentation

• Partner with PMs on what to build and how to measure it: North Star and guardrail metrics, funnels, cohorts, opportunity sizing and A/B test design and readouts.
• Own experimentation standards, from randomization, power and pre-registered metrics to novelty effects and clear ship/kill decisions.
• Push back when the data doesn't support a decision, and propose what to do instead.

Lead and grow the team

• Manage analysts and data scientists: hiring, goals, reviews, coaching and hard performance conversations when needed.
• Allocate capacity across squads and run a clear intake and prioritization process with Product.
• Raise the quality bar by reviewing analyses, test designs and models before they reach stakeholders.

Communicate and influence

• Turn complex data into clear recommendations and actionable stories for leadership.
• Build trust with Product, Engineering and Business by being direct, timely and right.

What you bring

• 7-9 years in product analytics, data science or growth analytics, ideally in e-commerce, marketplaces or consumer tech at scale.
• 2+ years managing analysts or data scientists as direct reports, including hiring, reviews and handling underperformance. Mentoring alone doesn't count.
• Strong SQL and Python, plus a cloud warehouse (Big Query preferred) and a BI tool (Looker preferred).
• Deep experimentation skills, including knowing when results can't be trusted.
• Production experience applying LLMs to analytics or internal tools, and the safeguards you added when things went wrong.
• Product thinking and clear communication with senior stakeholders, even when the answer isn't what they wanted.

Nice to have

• Text-to-SQL, RAG or agent workflows over a semantic layer or metric catalogue.
• Building internal data tools or self-serve analytics products.
• Recommendation systems, personalization or search ranking.
• Causal inference (difference-in-differences, synthetic control, uplift modelling).