Lead Data Scientist | Scrabble
Posted on December 18, 2025
Job Description
<meta charset="UTF-8" />
<p><b>Lead Data Scientist | MoEngage</b></p>
<p><b>As part of the Data Science/Engineering team at</b></p>
<p><b>MoEngage, here are some things you can expect:</b></p>
<p><b>About the role:</b></p>
<p>• Take ownership and be responsible for what you build - no micromanagement</p>
<p>• Work with A players (some of the best talents in the country), and expedite your learning curve</p>
<p>and career growth</p>
<p>• Make in India and build for the world at the scale of 900 Million active users, which no other</p>
<p>internet company in the country has seen</p>
<p>• Learn together from different teams on how they scale to millions of users and billions of</p>
<p>messages.</p>
<p>• Explore the latest in topics like Data Pipeline, MongoDB, ElasticSearch, Kafka, Spark, Samza</p>
<p>and share with the team and more importantly, have fun while you work on scaling MoEngage.</p>
<p>Roles and Responsilibilites</p>
<p>1. Unified AI Strategy & Technical Leadership</p>
<p>• Design and own the end-to-end technical vision for our next-generation marketing platform,</p>
<p>synthesizing recommender systems and LLMs into a cohesive architecture.</p>
<p>• Lead the research, design, and implementation of novel models that combine predictive signals</p>
<p>(e.g., "what to recommend") with generative capabilities (e.g., "why it's recommended").</p>
<p>• Establish and champion best practices across the full modeling stack, from classical ML</p>
<p>fundamentals to MLOps for both recommender and generative models.</p>
<p>• Act as the primary technical mentor for data scientists, providing guidance on everything from</p>
<p>feature engineering to fine-tuning LLMs.</p>
<p>2. Recommender System Innovation & Optimization</p>
<p>• Architect, build, and deploy large-scale recommender systems using a variety of techniques</p>
<p>(e.g., collaborative filtering, matrix factorization, content-based).</p>
<p>• Solve core recommendation challenges, including the cold-start problem, real-time</p>
<p>personalization, and balancing exploration vs. exploitation.</p>
<p>• Develop and implement rigorous offline and online (A/B testing) evaluation frameworks to</p>
<p>continuously measure and improve recommendation quality and business impact.</p>
<p>• Leverage classical machine learning models (e.g., XGBoost, Logistic Regression) to predict user</p>
<p>behavior (e.g., propensity to click, purchase, or churn) to be used as key features in the</p>
<p>recommendation engine.</p>
<p>3. Generative AI & LLM Integration</p>
<p>• Lead the development of LLM-powered features that enhance our platform, such as campaign</p>
<p>optimiser, creative generator, making customer data AI-ready with AI-generated metadata or</p>
<p>creating natural language interfaces for our entire product suite.</p>
<p>• Spearhead efforts in fine-tuning and adapting pre-trained LLMs on our proprietary data to</p>
<p>improve relevance, style, and factuality.</p>
<p>• Design and implement Retrieval-Augmented Generation (RAG) pipelines that allow LLMs to</p>
<p>reason over our vast product or content catalogs.</p>
<p>4. Cross-Functional Influence & Execution</p>
<p>• Partner with Product, Engineering, and Design leaders to translate ambitious business goals</p>
<p>into a concrete technical roadmap.• Communicate complex technical ideas and results effectively to a broad audience, from junior</p>
<p>engineers to executive leadership.</p>
<p>• Drive projects from ideation to production, ensuring models are not only accurate but also</p>
<p>scalable, efficient, and maintainable.</p>
<p>Minimum Requirements</p>
<p>• Bachelor’s/Master’s degree or PhD in a quantitative field such as Computer Science, Statistics,</p>
<p>Mathematics, or equivalent practical experience.</p>
<p>• 7+ years of hands-on experience building and deploying machine learning models in a business</p>
<p>environment.</p>
<p>• Expert-level proficiency in Python and its data science libraries (e.g., pandas, NumPy, scikit-</p>
<p>learn, XGBoost, spark).</p>
<p>• Advanced proficiency in SQL for querying large and complex datasets.</p>
<p>• 2+ years of demonstrated, hands-on experience developing and deploying solutions using</p>
<p>Large Language Models (e.g., fine-tuning, RAG, prompt engineering).</p>
<p>• Proven track record of leading complex, end-to-end data science projects that have delivered</p>
<p>significant business impact.</p>
<p>Preferred Requirements</p>
<p>• Experience with cloud-based ML platforms / ML ops (e.g., AWS SageMaker, MLflow) and their</p>
<p>generative AI services</p>
<p>• Hands-on experience with vector databases</p>
<p>• Familiarity with frameworks like LangChain or LlamaIndex or Agent Development Kit for building</p>
<p>LLM applications.</p>
<p>• Knowledge of LLM operational concerns, including cost management, latency optimization, and</p>
<p>responsible AI principles (bias, fairness, safety)</p>
<p>At MoEngage, we respect and value differences. We believe that when people from diverse</p>
<p>backgrounds and perspectives collaborate, we create the most value – for our clients, our</p>
<p>employees, and society. We embrace diversity and uphold a strong set of values. We are</p>
<p>committed to inclusivity and take pride in providing equal opportunities for success and growth.</p>
<p>Employment at MoEngage is based solely on professional competence, skills, and experience.</p>
<p>We stand firmly against all forms of discrimination and support equal rights and opportunities</p>
<p>regardless of gender, ethnicity, abilities, age, identity, orientation or expression, marital status</p>
<p>(including pregnancy), religion and beliefs, or any other status protected by law.</p>
<p>It is our policy to comply with all applicable national, state, and local laws related to non-</p>
<p>discrimination and equal opportunity. MoEngage is truly a place where everyone can bring their</p>
<p>passions, authentic selves, and talents to work, collaborating to drive progress and solve</p>
<p>meaningful challenges.</p>