About Viral Nation
At Viral Nation, we specialize in building social-first ecosystems for brands to connect with the modern consumer journey. Our integrated solutions align strategy, talent, media, and technology with culturally relevant creativity to scale the world’s fastest-growing digital brands. Viral Nation offers a fluid, creative, and growth-oriented environment that will support your ambitions to apply your talents in an open, collaborative, and fast-paced culture. Our ability to stay at the forefront of the industry has fueled our success and will guide us in paving the path forward. We’re driven to push boundaries and think beyond today to deliver strategies, and we’re just getting started.
What you’ll do here:
- Recruit, onboard, develop, motivate and mentor a team of data and microservice engineers, data scientists, data analysts and MLOps professionals. Our objective is to build and grow a distributed high-performance team across Viral Nation Group globally.
- Lead data design planning and coordinating with all technical team members with detailed specifications and expectations in order to deliver quality results within deadlines.
- Objectively measure performance, velocity, impact and optimize data science and advanced analytics processes for optimal output.
- Continuously ship high quality data structures, microservices and ML models balancing competing priorities to ensure we ship exceptional features while also resolving defects and managing technical debt.
- Work closely with the Product, DevOps and Data Science teams, and co-own the technology and product roadmaps with the CTO and VP Product, and communicate with C-level staff, board members, clients and other stakeholders.
- A proven track record for coaching, managing and developing technical team leaders and engineers, partnering with HR to develop departmental career ladder and training plans to better facilitate talent growth & development within the team.
- Build incredible teams and processes to engineer and deliver high quality products.
- Become the key evangelist of the latest applicable technologies and drive innovative ideas, solutions and products through leadership and decisive action.
- A successful history in managing engineering projects from conception through design, development, deployment and maintenance, preferable in a SaaS based business.
- Experience in designing and delivering distributed, highly available, large scale, high performing enterprise SaaS applications.
- Deep organization capabilities, team oriented and result driven.
- Strong written and verbal communication skills.
What you’ll need to have:
- Advanced Degree in Computer Science or related field. MA/MSc, equivalent or higher is preferred.
- Must have 10+ years of data intelligence development experience used by enterprise size applications and customer facing products and services, preferably in a progressive and technology-oriented company.
- Must have 7+ years of people management and hands-on data modeling experience working with data scientists, microservice experts, data engineers and python engineering teams, to build micro services, APIs and data systems and applications.
- Strong knowledge of the concepts, tools, practices and techniques of product management, technical architecture and microservice engineering of highly scalable applications housed within an intelligent SaaS platform, powered by ML technologies.
- Strong knowledge of AWS AI services, React.js/Node.js, Python, Postgres and DynamoDB, specifically strong experience with Relational, NoSQL and graph databases.
- Strong ability to apply various analytical models to business use cases (NLP, Supervised, Un-Supervised, Neural Nets, etc.), and strong experience with Python ML tools, including scikit-learn, and Pandas or similar frameworks, and different deep neural networks architectures (RNN, CNN, GAN, seq2seq/Transformers) using a cloud ML stack.
- Strong knowledge of relational databases, NOSQL stores and graph database architectures used in highly available and scalable applications.
- Strong proficiency with Machine Learning concepts and modeling techniques to solve problems such as clustering, classification, regression, anomaly detection, simulation and optimization problems, and other statistical analytical techniques, data mining, and predictive modeling on large scale data sets.
- Strong experience with data visualization tools — Tableau, QuickSight etc. preferred.
- Experience with AWS services EC2, ECS, serverless computing, EBS, RDS, S3, IAM.
- Drive product analytics to understand feature usage, comprehension, and optimization.
- Excellent verbal and oral communication skills.