Lead Data Scientist | MoEngage

As part of the Data Science/Engineering team at

MoEngage, here are some things you can expect:

About the role:

• Take ownership and be responsible for what you build - no micromanagement

• Work with A players (some of the best talents in the country), and expedite your learning curve

and career growth

• Make in India and build for the world at the scale of 900 Million active users, which no other

internet company in the country has seen

• Learn together from different teams on how they scale to millions of users and billions of

messages.

• Explore the latest in topics like Data Pipeline, MongoDB, ElasticSearch, Kafka, Spark, Samza

and share with the team and more importantly, have fun while you work on scaling MoEngage.

Roles and Responsilibilites

1. Unified AI Strategy & Technical Leadership

• Design and own the end-to-end technical vision for our next-generation marketing platform,

synthesizing recommender systems and LLMs into a cohesive architecture.

• Lead the research, design, and implementation of novel models that combine predictive signals

(e.g., "what to recommend") with generative capabilities (e.g., "why it's recommended").

• Establish and champion best practices across the full modeling stack, from classical ML

fundamentals to MLOps for both recommender and generative models.

• Act as the primary technical mentor for data scientists, providing guidance on everything from

feature engineering to fine-tuning LLMs.

2. Recommender System Innovation & Optimization

• Architect, build, and deploy large-scale recommender systems using a variety of techniques

(e.g., collaborative filtering, matrix factorization, content-based).

• Solve core recommendation challenges, including the cold-start problem, real-time

personalization, and balancing exploration vs. exploitation.

• Develop and implement rigorous offline and online (A/B testing) evaluation frameworks to

continuously measure and improve recommendation quality and business impact.

• Leverage classical machine learning models (e.g., XGBoost, Logistic Regression) to predict user

behavior (e.g., propensity to click, purchase, or churn) to be used as key features in the

recommendation engine.

3. Generative AI & LLM Integration

• Lead the development of LLM-powered features that enhance our platform, such as campaign

optimiser, creative generator, making customer data AI-ready with AI-generated metadata or

creating natural language interfaces for our entire product suite.

• Spearhead efforts in fine-tuning and adapting pre-trained LLMs on our proprietary data to

improve relevance, style, and factuality.

• Design and implement Retrieval-Augmented Generation (RAG) pipelines that allow LLMs to

reason over our vast product or content catalogs.

4. Cross-Functional Influence & Execution

• Partner with Product, Engineering, and Design leaders to translate ambitious business goals

into a concrete technical roadmap.• Communicate complex technical ideas and results effectively to a broad audience, from junior

engineers to executive leadership.

• Drive projects from ideation to production, ensuring models are not only accurate but also

scalable, efficient, and maintainable.

Minimum Requirements

• Bachelor’s/Master’s degree or PhD in a quantitative field such as Computer Science, Statistics,

Mathematics, or equivalent practical experience.

• 7+ years of hands-on experience building and deploying machine learning models in a business

environment.

• Expert-level proficiency in Python and its data science libraries (e.g., pandas, NumPy, scikit-

learn, XGBoost, spark).

• Advanced proficiency in SQL for querying large and complex datasets.

• 2+ years of demonstrated, hands-on experience developing and deploying solutions using

Large Language Models (e.g., fine-tuning, RAG, prompt engineering).

• Proven track record of leading complex, end-to-end data science projects that have delivered

significant business impact.

Preferred Requirements

• Experience with cloud-based ML platforms / ML ops (e.g., AWS SageMaker, MLflow) and their

generative AI services

• Hands-on experience with vector databases

• Familiarity with frameworks like LangChain or LlamaIndex or Agent Development Kit for building

LLM applications.

• Knowledge of LLM operational concerns, including cost management, latency optimization, and

responsible AI principles (bias, fairness, safety)

At MoEngage, we respect and value differences. We believe that when people from diverse

backgrounds and perspectives collaborate, we create the most value – for our clients, our

employees, and society. We embrace diversity and uphold a strong set of values. We are

committed to inclusivity and take pride in providing equal opportunities for success and growth.

Employment at MoEngage is based solely on professional competence, skills, and experience.

We stand firmly against all forms of discrimination and support equal rights and opportunities

regardless of gender, ethnicity, abilities, age, identity, orientation or expression, marital status

(including pregnancy), religion and beliefs, or any other status protected by law.

It is our policy to comply with all applicable national, state, and local laws related to non-

discrimination and equal opportunity. MoEngage is truly a place where everyone can bring their

passions, authentic selves, and talents to work, collaborating to drive progress and solve

meaningful challenges.