Engineering Manager - Data Science

About MoEngage

MoEngage is an insights-led customer engagement platform trusted by 1,350+ global consumer brands, including McAfee, Flipkart, Domino’s, Nestle, Deutsche Telekom, and OYO. MoEngage combines data from multiple sources to help brands gain a 360-degree view of their customers.MoEngage Analytics arms marketers and product owners with insights into customer behavior. Brands can leverage MoEngage Personalize to orchestrate journeys and build 1:1 conversations across the website, mobile, email, social, and messaging channels. MoEngage Inform, the transactional messaging infrastructure, helps unify promotional and transactional communication to a single platform for better insights and lower costs. MoEngage’s AI Suite helps marketers develop winning copies and creatives, optimize campaigns and channels that boost engagement, and help with faster execution.

For over a decade, consumer brands in 60+ countries have been using MoEngage to power digital experiences for over a billion monthly customers. With offices in 15 countries, MoEngage is backed by Goldman Sachs Asset Management, B Capital, Steadview Capital, Multiples Private Equity, Eight Roads, F-Prime Capital, Matrix Partners, Ventureast, and Helion Ventures.MoEngage was named a Contender in The Forrester Wave™: Real-Time Interaction Management, Q1 2024 report, and Strong Performer in The Forrester Wave™ 2023 report. MoEngage was also featured as a Leader in the IDC MarketScape: Worldwide Omni-Channel Marketing Platforms for B2C Enterprises 2023.

As part of the Data Science/Engineering team at MoEngage, here are some things you can expect:

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

Key Responsibilities

1. People Leadership & Advanced Technical Mentorship

• Lead, manage, and grow a team of data scientists, fostering their careers through active coaching and mentorship.
• Provide expert, hands-on guidance to your team on the nuances of designing, building, and evaluating classical ML models, AI agents and complex LLM chains.
• Own the hiring and development process, attracting top-tier talent that can thrive in a fast-paced, research-oriented environment.
• Balance your time effectively between managing team execution, providing mentorship, and making direct, hands-on contributions to the codebase.

2. Hands-On Architecture & Prototyping

• Strong experience of classical ML models, Reinforcement learning and LLM based models.
• Spearhead the rapid prototyping of new agent-based LLM concepts to quickly validate product ideas and de-risk technical challenges.
• Conduct deep, technical code reviews and design sessions to ensure the teams output is robust, scalable, and pushes the boundaries of whats possible.
• Stay on the cutting edge of LLM and AI agent research, guiding the team on adopting new models, techniques, and frameworks.
• 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. Unified AI Strategy and 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 its recommended").
• Partner with Product and Engineering leadership to define a bold but achievable roadmap.
• Translate ambiguous business goals into concrete technical requirements and a clear project plan for the team.
• Drive projects from ideation to production, ensuring models are not only accurate but also scalable, efficient, and maintainable.
• Develop and implement rigorous offline and online (A/B testing) evaluation frameworks to continuously measure and improve recommendation quality and business impact.

Minimum Qualifications

• Bachelor’s/Master’s degree or PhD in a quantitative field such as Computer Science, Statistics, Mathematics, or equivalent practical experience.
• 10+ years of hands-on experience building and deploying machine learning models in a business environment.
• 2+ years of experience in a formal management or tech lead role.
• Demonstrated, hands-on experience designing and building applications using classical ML models and Large Language Models (LLMs).
• Expert-level proficiency in Python, SQL and its data science libraries (e.g., pandas, NumPy, scikit-learn, XGBoost, spark).
• Proven track record of leading complex, end-to-end data science projects that have delivered significant business impact.

Preferred Qualifications

• Experience with cloud-based ML platforms / ML ops (e.g., AWS SageMaker, MLflow) and their generative AI services
• 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)
• Experience building systems that require both predictive and generative models to work in concert.

Why Join Us!

At MoEngage, we are passionate about our team and technology - see below to know more about us.

Life@MoEngage

Tech@MoEngage

Scale @MoEngage

We handle more than a billion messages every day. Rest assured, you will be surrounded by really smart and passionate people as we scale much more to build a world-class technology team.