Data Science Backend Engineer
2 weeks ago
Data Science Backend Engineer at Weekday
This role is for a Data Science Backend Engineer at Weekday.
As a Data Science Backend Engineer focusing on Data Science, you will be responsible for building, optimizing, and maintaining server-side applications that support data processing and machine learning models. You will collaborate closely with data scientists, data engineers, and other developers to create efficient data pipelines, scalable APIs, and robust backend services that power data-driven applications.
Key Responsibilities:
- Data Processing: Design, develop, and maintain scalable backend systems, services, and APIs that handle large datasets and machine learning models.
- Data Pipeline Integration: Collaborate with data scientists to integrate machine learning models and process large volumes of structured and unstructured data efficiently.
- API Development: Develop RESTful APIs to expose data science models and data-driven services to internal and external applications.
- Database Management: Design and optimize database structures for storing and querying large datasets ensuring high performance and scalability.
- Collaboration: Work closely with data engineers, data scientists, and frontend developers to deliver end-to-end data solutions.
- Performance Tuning: Identify bottlenecks and implement solutions for high performance and low latency in data processing systems.
- Security: Implement security best practices to protect sensitive data and ensure compliance with relevant regulations.
Required Skills and Qualifications:
- Programming Languages: Proficiency in languages such as Python, Java, or Node.js.
- Data Science Integration: Familiarity with integrating data science libraries such as TensorFlow, Scikit-Learn, or PyTorch.
- API Development: Experience with building and consuming RESTful APIs and microservices architecture.
- Database Management: Strong knowledge of SQL and NoSQL databases (e.g. PostgreSQL, MongoDB).
- Data Pipeline Tools: Experience with data processing tools and frameworks like Apache Kafka, Spark, or Hadoop.
- Cloud Platforms: Experience with cloud environments like AWS, GCP, or Azure for deploying data-centric applications.
- Version Control: Proficiency with version control systems like Git.
Preferred Skills:
- Containerization: Familiarity with containerization (Docker) and orchestration (Kubernetes).
- Data Streaming Tools: Experience with data streaming tools such as Apache Flink or Storm.
- Machine Learning Lifecycle: Strong understanding of machine learning lifecycle and model deployment.
- Data Governance: Familiarity with data governance, privacy laws, and data security best practices.
datagovernance,aws,data science,mongodb,apache flink,storm,gcp,nosqldatabases,node.js,data security,azure,pytorch,spark,sql,machinelearning lifecycle,machine learning,scikit-learn,python,restfulapis,docker,java,models,kubernetes,tensorflow,hadoop,postgresql,git,apachekafka
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