Data Scientist/ Data Architect
About The Position
DDN is the global leader in AI and data intelligence infrastructure, powering many of the world's most demanding AI, HPC, and data-intensive environments. Our customers include leading enterprises, research institutions, government agencies, and AI innovators that rely on DDN technology to accelerate discovery, innovation, and business outcomes. We are seeking a highly motivated Data Scientist / Data Architect to join our team and help shape the future of data-driven products, AI platforms, and enterprise analytics. This role combines data science, machine learning, data engineering, and enterprise architecture to deliver scalable solutions that transform data into strategic business value. As a Data Scientist / Data Architect, you will work at the intersection of AI, data platforms, cloud infrastructure, and business strategy. You will design and implement modern data architectures while developing analytics and machine learning solutions that support operational excellence, customer success, product innovation, and business growth. The ideal candidate combines strong technical depth in data science and architecture with the ability to engage stakeholders, translate business requirements into technical solutions, and drive projects from concept through production deployment.
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related field.
- 8+ years of experience in data science, data architecture, analytics engineering, or related disciplines.
- Strong proficiency in Python and SQL.
- Experience building and deploying machine learning models in production environments.
- Deep understanding of data modeling, ETL/ELT pipelines, and modern data platform architectures.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Hands-on experience with distributed data processing technologies such as Spark, Databricks, Snowflake, BigQuery, or equivalent platforms.
- Strong knowledge of statistics, experimentation, forecasting, and predictive analytics.
- Excellent communication and stakeholder management skills.
- Experience working with AI platforms, cloud infrastructure, SaaS products, or large-scale distributed systems.
- Experience with MLOps, DataOps, CI/CD, and model lifecycle management.
- Familiarity with vector databases, retrieval systems, LLMs, and generative AI architectures.
- Experience with Kubernetes, containerized environments, and cloud-native platforms.
- Knowledge of data governance, security, privacy, and regulatory frameworks.
- Experience leading enterprise-scale data transformation initiatives.
Responsibilities
- Develop machine learning and AI solutions to solve business and operational challenges.
- Design, build, validate, and deploy models for forecasting, anomaly detection, customer analytics, capacity planning, and product intelligence.
- Apply statistical analysis and experimentation techniques to generate actionable insights.
- Develop dashboards, visualizations, and executive-level reporting to communicate findings and recommendations.
- Monitor model performance and support continuous improvement initiatives.
- Partner with business stakeholders to define key metrics, KPIs, and success measures across products and operations.
- Design scalable enterprise data architectures supporting structured, semi-structured, and unstructured data workloads.
- Define data models, metadata standards, governance frameworks, and architectural best practices.
- Architect modern data platforms leveraging cloud, hybrid-cloud, lakehouse, and distributed data technologies.
- Establish data integration strategies across CRM, ERP, product usage, support, operational, and business systems.
- Build scalable ETL/ELT pipelines and data services that support analytics and AI workloads.
- Drive adoption of data quality, lineage, security, privacy, and compliance standards.
- Partner with product, engineering, and business leaders to identify high-value AI and analytics opportunities.
- Build reusable data products, semantic layers, and self-service analytics capabilities.
- Support AI initiatives involving LLMs, RAG architectures, vector databases, and enterprise knowledge systems.
- Collaborate with software engineering teams to operationalize analytics and AI capabilities in production environments.
- Contribute to the development of intelligent platform features that improve customer experience and operational efficiency.
- Serve as a trusted advisor on data strategy, architecture, and analytics best practices.
- Lead technical design reviews and architecture discussions.
- Mentor data scientists, data engineers, and analysts.
- Partner with stakeholders across Product, Engineering, Operations, Customer Success, Finance, and Executive Leadership.
- Communicate technical concepts and recommendations to both technical and non-technical audiences.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
