Associate Director, Engineering Data Science

Razorpay is one of Indias leading full-stack financial technology companies powering the way businesses move manage and grow money. Founded in 2014 by Harshil Mathur and Shashank Kumar with a simple vision to simplify payments for Indian businesses weve since grown into a fintech powerhouse driving Indias digital payment revolution.

Razorpay powers millions of businesses with a smarter scalable stack that goes beyond transactions to help them truly build and grow.

From seamless checkouts to payroll automation across India Singapore and Malaysia weve been engineering a fintech ecosystem thats redefining how money moves across Asia and were just getting started.

Today that ecosystem supports everyone from early-stage startups to some of Indias largest enterprises enabling them to accept process and disburse payments at scale while expanding into new ways of managing money more efficiently.

Our scale speaks volumes: Razorpay processes $180 billion in annualized transactions powering leading businesses like Airbnb Facebook WhatsApp Airtel CRED BookmyShow Zomato Swiggy Lenskart Mirae Asset Capital markets Indian Oil National Pension Scheme and over 100 of Indias unicorns. With strong roots in India and growing operations in Southeast Asia we are shaping the next chapter of financial technology across the region.

We are backed by global investors including GIC Peak XV Partners (formerly Sequoia Capital India & SEA) Tiger Global Ribbit Capital Matrix Partners MasterCard and Salesforce Ventures having raised over $740 million to date. Strategic acquisitions including Ezetap (POS and offline payments) Curlec (Malaysia expansion) BillMe (digital invoicing) and POP (rewards-first UPI) along with earlier moves in fraud prevention payroll and lending have further strengthened our platform and widened our footprint across Asia.

But what truly sets Razorpay apart is our culture. At Razorpay ownership is our oxygen you own what you build with no micromanagement or red tape just the runway to make your ideas fly. Learning is a lifestyle if youre curious youll feel at home here. People > Pedigree we hire for attitude hustle and hunger more than degrees. Transparency thrives over titles this is where interns question CXOs and CXOs say thank you. Guided by our values of Customer First Autonomy & Ownership Agility with Integrity Transparency Challenging the status quo and a strong belief that Razorpay grows with Razors youll be part of a 3000 strong team building not just products but the financial infrastructure of the future.

We are seeking a highly experienced and visionary leader for our Data Science team to lead and shape the future of products using Machine Learning and other data backed initiatives. You will be responsible for driving the strategic direction of our data science practice overseeing a team of talented data scientists ML Engineers and delivering innovative solutions to complex business problems. The ideal candidate should have a strong background in data science exceptional leadership skills and a proven track record of driving data-driven strategies and insights.

This role will report to the VP Engineering for Data.

Roles and Responsibilities:

• Leadership: Provide strategic direction and leadership to the data science team comprising about 13-15 individuals (Data Scientists ML Engineers MLOps Engineers) guiding them in delivering high-quality and innovative solutions. Collaborate with Product Management Engineering and Program Management to define the data science vision and roadmap aligned with business goals. Stay abreast of the latest advancements in data science machine learning and artificial intelligence and evaluate their potential impact on business operations.
• Team Management: Lead a high-performing data science team including data scientists machine learning engineers and MLOps. Foster a collaborative and inclusive team culture promote professional growth and mentor team members to enhance their skills and capabilities. Set clear goals and performance expectations engage the team in regular conversations and ensure the teams work aligns with the organizations vision and objectives.
• Project Management: Oversee end-to-end execution of data science projects from problem formulation model deployment tuning and operating them to deliver value to the business. The candidate should be able to break multi-quarter projects into fortnightly milestones and come up with a traceable Project plan. Drive the Quarterly planning exercise to arrive at the right OKRs and projects.
• Collaboration: Collaborate with cross-functional teams including engineering product management analytics and program management to identify opportunities and deliver data-driven insights and solutions. Drive the integration of data science capabilities into various business processes and systems. This role requires collaboration with multiple stakeholders and the candidate should be comfortable managing expectations across the board.
• Technical Expertise: Stay abreast of the latest advancements and trends in data science machine learning and AI technologies. Provide technical guidance and expertise to the team ensuring best practices in data analysis model development and evaluation.
• Strategy and Innovation: Work closely with the executive team to define the data science strategy and identify opportunities for leveraging data to drive innovation and create business value. Evaluate emerging technologies and techniques to enhance the teams capabilities and drive continuous improvement.
• Data Strategy and Governance: Collaborate with stakeholders to define data strategy including data acquisition management and governance standards and best practices for data collection storage quality and privacy ensuring compliance with relevant and implement data infrastructure and technology requirements to support data science initiatives.
• Performance Evaluation and Reporting: Establish metrics and key performance indicators (KPIs) to track the teams performance and measure the impact of data science initiatives.
• Continuous Improvement and Innovation: Identify emerging trends technologies and methodologies in the field of data science and champion their adoption within the organization. Promote a culture of continuous improvement and innovation encouraging experimentation exploring new techniques and driving efficiency gains. Establish and monitor metrics and KPIs to measure the impact and effectiveness of data science initiatives.

Mandatory Qualifications:

Core ML/DS Technical Skills

Machine Learning & Statistical Foundations (Must Have)

• Deep understanding of ML algorithms (supervised unsupervised reinforcement learning)
• Experience with deep learning frameworks (TensorFlow PyTorch)
• Strong grasp of statistical methods A/B testing and experimentation frameworks
• Time series forecasting Anomaly Detection Techniques Multi-arm bandit problems.
• Strong Command on Python and either of Java/C/Scala.

Exposure to Domain-Specific ML Applications (Must Have)

• Routing Optimization
• Fraud detection and prevention systems
• Risk modeling and credit scoring
• Recommendation systems
• NLP for customer support automation and document processing
• Real-time ML inference and streaming ML pipelines

Platform & Infrastructure

ML Engineering & MLOps (Must Have)

• ML model lifecycle management (training versioning deployment monitoring)
• Feature engineering platforms and feature stores
• Model serving infrastructure (batch and real-time)
• ML observability drift detection and model performance monitoring
• CI/CD for ML systems MLFlow SageMaker Airflow.

Data Infrastructure

• Distributed computing frameworks (Spark PySpark Flink) (Must Have)
• Data warehousing solutions (Redshift Snowflake BigQuery)
• Streaming platforms (Kafka Kinesis) (Optional)
• Understanding of data lake architectures (Iceberg Delta Lake) (Optional)

Engineering Fundamentals

• Strong programming skills (Python Scala or Java)
• System design for high-scale low-latency applications
• Cloud platforms (AWS/GCP/Azure) and containerization (Docker Kubernetes)
• SQL and data modeling expertise

Good to Have

• Masters or Ph.D. in a quantitative field such as Computer Science Data Science Statistics Mathematics or related disciplines.
• Minimum of 12 years of experience in data science machine learning with a track record of delivering data-driven solutions in a leadership capacity.
• Proven experience in leading and managing a team of data scientists and driving successful project delivery. Strong ability to inspire motivate and mentor team members.
• Excellent communication skills with the ability to effectively communicate complex technical concepts to both technical and non-technical stakeholders. Demonstrated ability to collaborate and build relationships with diverse teams.
• Strong business understanding and the ability to translate business objectives into data science projects and deliverables. Experience in aligning data science initiatives with organizational goals and driving measurable outcomes.
• Ability to think strategically and provide thought leadership in data science and AI. Proven experience in developing data science strategies and roadmaps that support