PhonePe is seeking a highly analytical and driven Data Scientist to join our team. This role requires a minimum of one year of professional experience and a robust foundation in scalable machine learning implementations.

The successful candidate will play a pivotal role in designing and deploying sophisticated models and reusable ML frameworks that improve productivity and help drive strategic business initiatives.

Core Responsibilities

Framework & Platform Development: Design, build, and deploy reusable machine learning frameworks (e.g., config-driven modeling platforms) that generalize across multiple business use cases, rather than one-off model builds — enabling horizontal use cases to be onboarded without rebuilding the pipeline each time.

Model Development & Deployment: Design, develop, and deploy scalable machine learning models and frameworks across diverse environments, encompassing offline batch processing, real-time online systems, and edge computing architectures, with a strong emphasis on production deployment and operationalization.

Horizontal Engineering Partnership: Act as the point of contact with horizontal engineering teams building shared Data Science infrastructure such as  implementing new capabilities, communicating Data Science requirements, and validating that a platform usability and maturity before rolling it out broadly to the larger data science team. Work with the Data Engineering team on communicating storage and warehousing requirements, and help create development environments usable across Data Science workflows, standardizing common pipelines rather than letting usage splinter across teams.

Model Deployment & Serving: Maintain working knowledge of how model deployment works internally (e.g., Docker, MLflow serving), and act as the point of contact with the engineering team that builds the platform for online scoring/model deployment, ensuring Data Science requirements are reflected in the platform's roadmap.

Advanced Data Quality Monitoring : Conduct rigorous analysis of complex, high-volume datasets to extract actionable insights and inform strategic business decisions. Identify problems with data ingestion, data quality, severity of issues, and downstream impact on models and workflows using the data.

Cross-Functional Collaboration: Partner with engineering, product, and business stakeholders to define core challenges and formulate robust, data-driven solutions, including identifying where a common framework can serve multiple teams instead of a team-specific model.

Technical Communication: Synthesize and present complex quantitative findings, methodologies, and strategic recommendations to both technical and non-technical executive stakeholders.

Qualifications

Required Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a closely related quantitative discipline.

• Experience: A minimum of 3 to 5 years of professional experience in a Data Scientist or ML Engineer capacity, with a track record of deploying machine learning algorithms and/or ML frameworks into production environments.

• Core Languages: Advanced proficiency in Python and SQL.

• MLOps & Infrastructure: Familiarity with model lifecycle management tools (e.g., MLflow), model deployment/serving fundamentals (e.g., Docker, MLflow serving), CI/CD for ML pipelines, and modern database architectures.

• Analytical Foundations: Deep conceptual and practical understanding of traditional machine learning algorithms, statistical modeling, linear algebra, and probability theory

• Programming & Tools: Working knowledge of Java and experience with version control and CI/CD systems (Git).

• Engineering for Scale: Experience building reusable, config-driven or plugin-based systems (as opposed to single-purpose models), with an eye toward adaptability across use cases.

• Problem Solving: Exceptional analytical reasoning, with the ability to operate independently and navigate ambiguous data challenges.

• Communication: Outstanding written and verbal communication skills, tailored to diverse professional audiences.

Preferred Qualifications & Technical Competencies

Candidates possessing the following supplementary skills will be given strong consideration:

• Big Data & Distributed Computing: Deep knowledge and practical experience with PySpark and other big data processing frameworks.

• Deep Learning: Proficiency leveraging neural network frameworks such as TensorFlow or PyTorch.

• Computer Vision: Working knowledge of Computer Vision concepts and algorithms.