Lead I - Software Engineering - Data Science
- UST
- 5 - 7 years
- Bengaluru
- 1 month ago
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Job Description
About the Role:
We are seeking a Senior Software Engineer to join our Data Science Engineering team to develop the next generation of data-driven solutions for our rapidly growing e-commerce platform. This role sits at the vital intersection of data engineering and machine learning, empowering personalization, intelligent recommendations, dynamic pricing, and logistics optimization. You'll work closely with data scientists and engineers to productionize ML models, enabling smarter and faster decision-making that impacts millions of users daily.
Key Responsibilities:
Design and implement scalable real-time and batch data pipelines to support key e-commerce initiatives, including personalization, fraud detection, supply chain optimization, and marketing automation.
Collaborate with data scientists to deploy and maintain machine learning models in production environments, ensuring performance, robustness, and observability from the outset.
Build reusable frameworks and services for feature engineering, model training, and inference across the customer journey (search, browse, cart, checkout, delivery).
Develop and maintain tools and infrastructure for ML observability (e.g., drift detection, performance monitoring, automated retraining triggers).
Ensure data integrity and lineage through robust testing, logging, and metadata tracking practices.
Work with cross-functional stakeholders (Product, Analytics, Engineering) to align data products with business objectives.
Mentor junior team members and help shape technical standards and best practices across the Data Science Engineering function.
Must-Have Qualifications:
Bachelor's or Master's degree in Computer Science, Software Engineering, or related technical field.
5+ years of professional experience in backend or distributed systems development.
2+ years of hands-on experience developing data or ML infrastructure at scale (e.g., e-commerce, fintech, adtech).
Proficiency in Python, with backend development experience in Java, Scala, or Go.
Expertise in distributed data systems such as Apache Spark, Flink, Kafka, and orchestration tools like Airflow or Dagster.
Hands-on experience with cloud-native infrastructure (AWS, GCP, or Azure), containerization (Docker/Kubernetes), and infrastructure-as-code tools.
Experience with modern data warehousing solutions such as BigQuery, Snowflake, and feature stores for ML applications.
Solid understanding of the end-to-end ML lifecycle, including tools like MLflow, SageMaker, TFX, or Kubeflow.
Good-to-Have Qualifications:
Direct experience with recommender systems, ranking models, or real-time personalization engines in an e-commerce setting.
Familiarity with experimentation and A/B testing platforms (e.g., Optimizely, Amplitude Experiment).
Domain experience in fraud detection, customer segmentation, or logistics optimization using machine learning.
Experience building CI/CD pipelines specifically for data and ML workflows.
Contributions to open-source data/ML libraries or platforms.
What We Offer:
A high-impact role in a fast-paced, data-first e-commerce environment.
The opportunity to work with petabyte-scale datasets and cutting-edge ML infrastructure.
A collaborative and inclusive culture that encourages innovation, autonomy, and continuous learning.
Competitive salary, equity opportunities, and comprehensive health and wellness benefits.
Required Skills
Machine Learning,Python,Data Warehouse Architecture,ML workflows
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Engineering - Software & QARole Category: Software DevelopmentRole: Data Platform EngineerEmployement Type: Full timeContact Details:
Company: USTLocation(s): Bengaluru
Keyskills: continuous integration kubernetes data scala golang ci/cd data warehousing docker java spark gcp end backend bigquery ml cd snowflake python development microsoft azure backend development machine learning apache flink system infrastructure kafka data warehousing concepts aws
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